Alliances of Networks and Networks of Alliances:
International Cooperation in Mobile Telecommunications
by
Olaf Rieck*, Chen Yinzhi, Haneesa Habib, and Xiao Junzheng
Nanyang Technological University
Abstract
A recent trend observed in the telecommunications industry is the rise in the
number of roaming agreements. A roaming agreement is an agreement
between two mobile operators from two different countries. It governs the
conditions under which a mobile subscriber from one country can use mobile
services in another country. Today most carriers have agreements with the
majority of mobile carriers of other countries and often even with multiple
carriers from the same foreign country. Roaming agreements typically differ
in terms of the number and type of service offered to the roaming subscriber,
as well as in the financial terms and conditions specified in the agreement. As
a result, mobile subscribers roaming abroad often have the choice been a
variety of alternative roaming carriers whose services may vary in both their
scope and their rate structure.
The increased demand for roaming services and the need for inter-carrier
cooperation to provide these services has lead to the formation of international
roaming alliances between mobile carriers. Examples for such roaming
alliances include Starmap, Free Move, or the Asia Mobile Initiative. In this
paper we employ tools and methods developed for Social Network Analysis to
empirically study the implications of roaming alliance formation on roaming
charges. We also study the emergence of implicit roaming partnerships, that
is, networks of carriers which provide each other’s subscribers favourable
roaming charges without actually being organized any of the formal (or
explicit) roaming alliance. Finally, we attempt to explain the structure and
composition of alliances and implicit partnerships using a variety of
explanatory variables, including macroeconomic indicators, location factors,
and company-specific measures.
The findings of our paper may be useful for assessing the stability of the
emerging roaming alliances and for predicting the future market structure of
the international mobile telecommunications industry.
* Address correspondence to Olaf Rieck, Assistant Professor, S3-B1b-55, Nanyang
Avenue, Singapore 639798 (e-mail: [email protected], telephone: +65 6790-5929, fax:
+65 6792-4217. Our thanks go to Kim Changsu. All errors are ours.
-0-
1. INTRODUCTION
A recent trend observed in the telecommunications industry is the rise in the number of
roaming agreements. A roaming agreement is an agreement between two mobile operators
from two different countries. It governs the conditions under which a mobile subscriber
from one country can use mobile services in another country. In particular, the roaming
agreement specifies the interconnection charge, that is, the fee which a mobile operator in a
foreign country receives from the home country operator for originating a roaming call.
Most mobile operators offer roaming services to most foreign countries. Typically they
have roaming agreements with at least one (if not more) operators in any of these countries.
The agreements may however vary in their financial terms and conditions. Also, a carrier
may impose different roaming charges on their subscribers, depending on the operator they
select when roaming in a foreign country. The roamer, that is, the mobile subscriber who
uses international roaming services in a foreign country, therefore may have the option of
choosing between different operators with services offered at different charges. Given the
lack of other distinguishing features between these operators, the informed roamer will be
inclined to choose the roaming carrier with the lowest charge.
There are various reasons why an operator may set differential roaming charges for using
the services of operators located the same foreign country. One may be that the roaming
charge simply reflects an underlying interconnect charge that both carriers negotiated. If an
operator managed to negotiate a lower interconnect charge with one foreign operator than
with others, this cost advantage on to the customer in order to attract them to the service
that imposes the least cost to their home operator.
Second, by offering a lower end-user roaming charge for roaming on a particular foreign
network a mobile operator can strategically favour one roaming partnership over another.
The carrier may thereby try to attain reciprocation by foreign carrier, i.e., the foreign
mobile operator may also set roaming charges such as to favour the same partnership. Such
mutual arrangement, which may be negotiated or tacit, may help to secure market shares in
the highly lucrative international roaming markets.
-1-
A similar idea – carriers mutually offering each others’ customers better service when
roaming abroad – is also the stated reason for a current trend in the mobile
telecommunications industry: the formation of international roaming alliances.
Up to the completion date of this study, six mobile GSM roaming alliances had been
formed. However, this number pales in comparison to the total number of individual interoperator roaming agreements.
The purpose of this paper is to explore the emergence of international collaboration and
alliance formation in roaming markets. By investigating roaming relationships and the
relative roaming charges that operators impose on their customers we attempt to identify
explicit and tacit collaborations between operators. Once such collaborations are identified,
the questions arising are (a) whether there exist systematic patterns in the network of
international roaming agreements (i.e., are there groups of carriers which tend to grand each
others’ customers favourable conditions?); (b) if such groups exist, whether there are
systematic factors influencing the group formation (for example, do large carriers group
with large carriers, or do large carriers tend to group up with a set of smaller carriers?); and
(c) whether such groups are congruent with the currently forming alliance groups (i.e., do
current roaming alliances engage in strategic pricing behaviour in that they grand each
other’s customers lower roaming charges, or are current roaming alliances merely a
marketing tool?).
Past failures of telecommunications alliances (for example, Global One, Concert) have
discouraged alliance formation in this industry for almost half a decade. Understanding the
fundamental factors driving strategic alliance formation and applying the lessons learnt
may help carriers increase the probability of future alliance success.
Section two of this paper reviews the existing academic literature on alliance formation. In
section three we outline our research methodology. In section four we describe our data
analysis and present the results. Section five summarizes and concludes. Appendix A lists
all mobile operators analyzed in this study. Finally, Appendix B briefly describes currently
existing mobile operator alliances.
-2-
2. LITERATURE REVIEW ON ALLIANCE FORMATION
Throughout the past two decades, a large body of academic literature on the formation of
strategic alliances has been created. The literature spans research from a variety of
perspectives, including the strategic management perspective, the organizational
perspective, and the economic perspective. All three perspectives have extensively
investigated the motivations behind alliance formation. Broadly speaking, these
motivations can be classified into three different categories: scale, transaction cost, and
learning.
2.1 Scale
The alliance literature has always considered size as a firm-specific as well as a countryspecific characteristic that influences both a firm’s strategy for alliance formation and its
choice of alliance partners. The size of the alliance partner firm also allows inferences as to
its embeddedness1 and position within its business network, and as to its access to
information. Access to information can determine a firm’s opportunities or constrain its set
of alliances choices [Gulati, 1998].
Firm size influences a firm’s resource capabilities, which affects the firm’s opportunities as
well as its competitiveness in the areas of scale related savings, such as economies of scale,
ability to survive in an uncertain environment, and the ability to expand both domestically
and globally. Differences in the firm’s capabilities also influence its need and propensity to
cooperate with foreign and/or larger firms and its choice of alliance partners [Zaman and
Mavondo, 2001].
Small firms are highly inclined towards forming alliances to ensure their survival. In
particular, smaller entrants have an incentive to cooperate with incumbents or larger firms
in the industry. For example, in the telecommunications industry smaller mobile operators
may in future enter into alliances to expand without acquiring costly licenses. Given their
1
Embeddedness refers to the fact that exchanges and discussions within a group typically have a history, and
that this history results in the routinization and stabilization of linkages among members. [Marsden, 1981].
-3-
limited financial resources, this may be the only viable both to compete against global
market leaders like Vodafone, who are able to obtain multiple licences on their own. On the
other hand, small firms are themselves often unattractive alliance partners due to a lack of
inimitable resource ownership, and their inability to increase competitiveness. [Hite and
Hesterly, 2001; Burgers et al, 1993].
Large firms with ample resources have less incentive to form alliances, even though they
are attractive alliance partner choices. Furthermore, alliances between large firms are likely
to come under close antitrust scrutiny, which further impedes alliances involving large
firms [Burgers et al, 1993].
The existing literature has therefore concluded that intermediate firms have the highest
alliance rates. They usually choose other intermediate firms as alliance partners, since large
firms are unlikely to favour alliances with other large or intermediate firms that could
become a future threat to their market position and their survival [Burgers et al, 1993].
In the telecommunications industry, each of the five existing GSM mobile alliances has a
different alliance structure. Observation has shown that the degree of similarity of alliance
partners’ size is an important factor for alliances to maintain their alliance relationship.
Mobile telecom alliances with at least one dominant operator, such as FreeMove (with TMobile and Orange) are seen to be less successful in terms of alliance survival as compared
to those without a dominant operator. This may be due to conflict of interests between the
dominant firm(s) and the rest of the alliance members. Moreover, the global corporate
strategy of T-Mobile and Orange resulted in overlapping markets and reduced the
effectiveness of the FreeMove alliance [Curwen and Whalley, 2004b; Park, 1997; Balfour,
2004; Oum et al, 2001]. Curwen and Whalley predicted that FreeMove will either remain as
a loose collaborative arrangement instead of developing into a tightly coordinated alliance.
Alternatively it may collapse, as its larger members seek to impose their view on others
[Curwen and Whalley, 2004b].
This is in contrast to the success of Starmap, whose members are of approximately the
same size, thereby increasing members’ incentive to agree on common strategies, products
-4-
and services. Alliance members are not direct competitors in the same market, ensuring the
continuity of the alliance.
According to Gerpott and Jakopin, the size and saturation level of the domestic market also
plays a part in the degree of internationalization of mobile operators, hence influencing
mobile alliance formation. The larger the market size and growth potential of the domestic
mobile market, the more conservative the internationalization strategy and thus the lower
the alliance rates [Gerpott and Jakopin, 2004]. For example, operators in countries like the
United States utilize a more conservative internationalization strategy, due to the high
domestic demand and a large, still unsaturated telecommunications market.
Additionally, the market size and growth potential of the countries’ tourism market and the
development of its international business environment (in particular, imports and exports)
are factors that influence cross-border roaming alliance formation. This is due to roaming
revenue being largely dependent on the number of international tourists and business
travellers to and from the country.
2.2 Transaction costs
The effect of alliances on transaction costs is another focal point of alliance research. The
Alliances may help a firm to reduce search costs, negotiation costs and enforcement costs.
A large body of literature has discussed and affirmed that firms enter alliance networks to
enjoy access to better information and opportunities, which results in a reduction of search
costs [Gulati et al, 2000; Soh, 2003].
Benefits gained from transaction costs reduction may be offset for every new linkage by the
strain placed on the firm’s absorptive capacity for a new alliance network link, as well as
by extra coordination efforts of management across new links. Therefore, the marginal
benefit (cost) of a new linkage is significantly lower (higher), after an optimal number of
links has been established in an alliance [Ahuja, 2000b]. Moreover, not all alliances provide
equal benefits to their members and some inter-firm relationships within an alliance are
simply better than others. For example, unequal roaming revenue distribution within an
alliance, due to differing popularity of countries as holiday destinations, may strain
relationships between alliance members.
-5-
Diversification is another significant factor influencing alliance formation. Less diversified
firms may benefit more from a cooperative relationship with a foreign firm than a more
diversified one, due to the potentially greater reduction of transaction costs for the former.
This is attributed to the relatively limited resource capabilities of the less diversified firm as
compared to its more diversified counterpart.
In the telecommunications industry, different forms of alliances emerged in the past.
“Converging alliances” appear to create more value and reduce potential conflicts as
partners compete in non-overlapping markets, while “pure play alliances” increase the
potential for conflict as partners compete in closely related markets [Raphael, 1998]. Some
industry-related alliance benefits include better control over roaming revenues (which
reduces negotiation and enforcement cost), better transfer of expertise between countries
(which reduces search cost), and one-time product development for a variety of markets
(which allows scale related savings) [Curwen and Whalley, 2004b; Park and Zhang, 2000].
2.3 Learning
A third well-researched area is the link between strategic alliances and organizational
learning. Besides operational and strategic incentives, learning as a means to sustain a
firm’s competitive advantage in the industry also serves as a significant incentive for
alliance formation [Iyer, 2002].
At the same time, with the uncertainty of unreciprocated information sharing by the
receiving party and a lack of trust with other firms, many firms hesitate to share
information for fear of helping potential future competitors. Therefore, the closeness and
strength of relationships are two important inter-organizational networking mechanisms
needed to mobilize information and resources [Soh, 2003]. The closer and stronger the
relationships between operators, the more willing the operators are to share information.
Firms that enter alliances with the primary aim of sharing explicit knowledge tend to
approach alliance formation from a complementary view and seek to identify visible,
matching knowledge-related capabilities that can be easily transferred and incorporated by
their firm. [Nielsen, 2000]. This is especially true in view of the emergence of 3rd
Generation (3G) technology in cellular networks. Mobile operators that are most effective
-6-
and efficient in gaining advanced knowledge, expertise, and experience in the area of 3G
technology are likely to be more attractive as potential alliance partners. Learning and
dissemination of such knowledge is critical to the survival of mobile operators in the new
era of technology developments.
3. RESEARCH METHODOLOGY
3.1 Objective and Basic Definitions
In the past five years a large number of international roaming agreements have been
formed. Most mobile carriers have a roaming agreement with the majority of major mobile
carriers in the world. If roaming agreements are interpreted as linkages between carriers,
the set of existing agreements forms a network of relationships between the mobile carriers.
Our research objective is to ascertain characteristic patterns underlying this network of
agreements and, motivated by the preceding theoretical discussion, the factors influencing
the formation and nature of these agreements.
These include firm-specific factors such as size and financial status as well as countryspecific conditions, such as popularity of the country as a location for international business
ventures or holiday destination.
We start our description of the research approach by providing a number of definitions,
which are important to understand the nature of the data we collected.
Definition 1: A partnership is a roaming agreement between two carriers A & B of two
different countries
Definition 2: A weak favoured partnership between carriers A and B is a partnership
whereby carrier A offers a lower roaming charge to carrier B than to any other carrier
competing in the same country-to-country roaming market. Note that weak “favoured
partnerships” are non-reciprocal, i.e., a weak “favoured partnership” between carriers A
and B does not imply a weak “favoured partnership” between carriers B and A.
Definition 3: A strong favoured partnership between carriers A and B is a reciprocated
weak “favoured partnership”, i.e., A has a weak “favoured partnership” with B and B has a
weak “favoured partnership” with A.
-7-
Favourite partnerships (weak or strong) may be coincidental, they can be tacit, or they can
be explicitly agreed upon. However, we believe that the existence of a strong favoured
partnership is typically not just coincidental, but typically at least tacitly deliberate.
We shall explore the characteristic patterns that are inherent in the global network of both
weak and strong favoured partnerships and study factors that may foster the formation of
such favoured partnerships.
3.2 Sample Design and the Data Collection
In order to determine whether a favourite partnership exists between two carriers, we
collected and compared the roaming charges that carriers from one country charge to all
roamers travelling to a specific foreign country. We collected data on roaming charges from
35 countries, which includes member countries of the Organisation for Economic Cooperation and Development (OECD) and the five “Tigers” of Asia, namely Singapore,
Hong Kong, Malaysia, Taiwan and Thailand.
Telecommunication networks in these countries are relatively advanced in international
roaming technology and the inclusion of the five “Tigers” would represent a more Asian
perspective. The source of the roaming charge data were the Internet websites of the mobile
operators. We focused on the “Global System for Mobile Communications” (GSM)
international roaming network as this network is currently the most widely used mobile
roaming network in the world; with over 600 operators in 212 countries/areas of the world
using this network (GSM Association).
Similar to domestic telephone charges, international roaming charges may often exhibit a
number of complexities. For example, carriers may charge different amounts for peak and
off-peak, for business and private, or for subscribed and prepaid. In order to make the data
collection process manageable, we restricted the collection by the following criteria:
(1) All roaming charges had to be listed the operator’s websites. They had to apply to
mobile voice calls from the visited country to a fixed line in the home country;
-8-
(2)
The charges had to apply to monthly subscribed service plans. If price differences
existed for different types of subscription plans, the data were collected from the
business user plans;
(3)
Charges had to apply to peak hours, typically within the hours between 7am and 7pm;
(4)
If a range of charges was stated, the highest stated charge was selected.
(5)
The charge had to be the first minute roaming charge, which may include additional
charges such as service charges and set up charges set by foreign operators.
The collection process was confined to the narrow window period between the beginning
of September and mid-November 2004. After careful search of the websites of 123
operators, the roaming charges of 93 operators were taken into our sample. A further five
operators were eliminated from the sample, as they were the “sole” operators left in their
country after the first elimination. Our data were hence unable to support any comparison
of roaming charges among two or more operators from the same country, making it
impossible to apply the concept of a favourite partnerships in a meaningful way.
In order to focus on the essential aspects we set out to study, we reduced the collected
database of roaming charges to two 88 by 88 matrices, which we named weak favoured
partnership matrix and strong favourite partnership matrix. The weak favoured partnership
matrix contains a ‘1’ in a field indicating the presence of a weak favoured partnership and a
‘0’ indicating that no such partnership existed between the two given carriers. The strong
favourite partnership matrix contains a ‘1’ in a field indicating the presence of a strong
favoured partnership and a ‘0’ otherwise. As strong favoured partnerships are symmetric,
so is the strong favourite partnership matrix. The weak favourite partnership matrix is nonsymmetric. Figure 1 shows an excerpt of the strong favoured partnership matrix for the
countries Austria and Australia. The diagonal country-blocks are blank, as roaming does
not exist between carriers within the same country. The matrix shows that not every carrier
necessarily is in a strong favourite partnership. However, carrier 2 of Austria and carrier 4
of Australia are in such a partnership, and so are carrier 4 of Austria and carrier 2 of
Australia. A network representation of the entire matrix is depicted in Figure 2.
-9-
A1
A2
A3
A4
AT 1
AT 2
AT 3
AT 4
AT 5
A1
0
0
0
0
0
A2
0
0
0
1
0
A3
0
0
0
0
0
A4
0
1
0
0
0
AT 1
0
0
0
0
-
AT 2
0
0
0
1
-
AT 3
0
0
0
0
-
AT 4
0
1
0
0
-
AT 5
0
0
0
0
-
Figure 1: Excerpt from the strong favoured partnership matrix
We also collected data used for a number of control country-specific and carrier- specific
control variables, including population size, GDP, GDP growth, number of international
travellers, exports, imports, carrier revenues and market share, and information about the
carriers’ membership in an existing international mobile alliance.
CHAPTER 4: DATA ANALYSIS
The collected data were analyzed in two steps. In the first step, we used social network
analysis to generate a number of indicators and descriptors of characteristic patterns in the
strong favoured partnership matrix. In particular, we computed each carrier’s degree
centrality2, checked for the existence of cliques3 formed by groups of operators, and
generated the cliques-related hierarchical clustering of overlapped matrix4.
In the second step we used these descriptors as dependent variables in a regression which
included the set of country-specific and carrier-specific control variables listed above. We
used linear regression analysis and multinomial logit regression analysis to assess the
impact of the independent variables on degree centrality and on the membership in a clique,
respectively.
2
An actor with a high degree centrality reflects many direct ties it has and thus holds an influential and
important position in the network since many other actors will want to seek and establish direct ties to him.
3
A clique is a subset of a network in which the actors are more closely and intensely tied to one another than
they are to other members of the network [Luce and Perry, 1949].
4
Hierarchical Clustering of Overlapped Matrix is a series of nested partitions of overlapped cliques that gives
an overview as to which operators are clustered together at each level of overlapped clique membership and
which operators are in isolation [Johnson, 1967].
- 10 -
Figure 2: Strong favoured partnership network
- 10 -
4.1 Social Network Analysis (SNA)
SNA is a descriptive analysis that depicts a network perspective. This network perspective
is built on the general notion that economic actions of an actor are influenced by the social
context in which it is embedded and that actions can be influenced by the position of the
actor (operator) in its social network [Gulati, 1998]. A social network is a set of nodes,
linked by a set of social relationships of a specified type [Laumann et al, 1978]. SNA was
developed to analyze such networks and to be able to describe their structure. In our study
we use SNA for the study of inter-firm relationships. There are a number of convenient
software packages that are specialized on conducting SNA. We used UCINET VI to
perform all our SNA related computations.
4.2 Degree Centrality
Degree Centrality measures the network activity of an actor (the operator) in
relation to the immediate ties (number of ‘1’s) that it has in the network [Freeman, 1979].
An actor with a high degree centrality shows that it has many direct ties, and thus is seen to
hold an influential and important position in the network, as many other actors will want to
seek and establish direct ties to him. This measure is an important dependent variable as it
reflects the influential powers of operators in the roaming network that are able to form
more “favoured partnerships” and thereby leverage more benefits than others.
The values of degree centrality for all operators in the sample are reported in Table 1.
Japan’s Vodafone Group member had the highest degree centrality in the sample with a
value of 18. It was followed by Oskar Mobil from Czech Republic and Vodafone member
from Hungary each with a degree centrality of 16 ties. Seven out of the 15 operators with
degree centrality of ten ties and above were part of the Vodafone Group and its Partner
Network. Only two of the 15 operators were from FreeMove Alliance, one was from
Bridge Mobile Alliance, and the rest were not involved in any of the previously mentioned
six alliances. Six of the FreeMove alliance members had zero degree centrality, while
another nine operators with zero degree centrality were not members of any alliance
network. Conversely, none of the members of the Vodafone Group and its Partner Network
had zero degree centrality.
- 11 -
4.598
4.598
4.598
4.598
3.448
3.448
3.448
3.448
3.448
3.448
3.448
3.448
2.299
2.299
2.299
2.299
2.299
2.299
2.299
2.299
2.299
2.299
2.299
1.149
1.149
1.149
1.149
1.149
0
0
0
0
0
0
0
0
0
0
0
0.01
0.01
0.01
0.01
0.007
0.007
0.007
0.007
0.007
0.007
0.007
0.007
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.005
0.002
0.002
0.002
0.002
0.002
0
0
0
0
0
0
0
0
0
0
0
Table 1: Degree Centrality Values
- 12 -
HON5
FRA2
SWI1
ITA4
HON3
UK1
USA1
USA2
Share
4
4
4
4
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
0
Normalized
Degree
LUX2
GER4
SIN2
THA4
HON6
HON4
SLO2
SWE3
BEL2
SWI2
DEN2
ITA2
TAI2
NET4
POL2
SIN1
DEN3
TAI6
TAI4
HUN1
JAP1
IRE2
AUT4
TAI3
CZE2
LUX3
FRA1
NET1
AUS1
BEL3
UK2
DEN1
UK4
POR2
AUT1
UK3
HUN2
FIN4
CAN2
Degree
Centrality
0.043
0.038
0.038
0.031
0.031
0.029
0.029
0.029
0.029
0.029
0.026
0.026
0.024
0.024
0.024
0.021
0.021
0.021
0.021
0.019
0.019
0.019
0.019
0.019
0.019
0.014
0.014
0.014
0.014
0.014
0.012
0.012
0.012
0.012
0.012
0.012
0.012
0.01
0.01
Share
Share
20.69
18.391
18.391
14.943
14.943
13.793
13.793
13.793
13.793
13.793
12.644
12.644
11.494
11.494
11.494
10.345
10.345
10.345
10.345
9.195
9.195
9.195
9.195
9.195
9.195
6.897
6.897
6.897
6.897
6.897
5.747
5.747
5.747
5.747
5.747
5.747
5.747
4.598
4.598
Normalized
Degree
Normalized
Degree
18
16
16
13
13
12
12
12
12
12
11
11
10
10
10
9
9
9
9
8
8
8
8
8
8
6
6
6
6
6
5
5
5
5
5
5
5
4
4
Degree
Centrality
Degree
Centrality
JAP2
CZE1
HUN3
FIN3
POL3
NET3
SLO1
LUX1
SIN3
AUS2
AUS4
ITA3
IRE3
DEN5
CAN1
HON1
POR3
THA2
UK5
GER2
SPA2
HON2
AUT5
BEL1
TAI1
SWI3
AUT2
FIN1
SWE5
GER3
AUT3
AUS3
POR1
CZE3
THA5
GER1
TAI5
DEN4
POL1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4.3 Cliques and Cliques-Related Hierarchical Clustering of Overlapped Matrix
Measures
A clique is a subset of a network in which the actors are more closely and intensely tied to
one another than they are to other members of the network [Luce and Perry, 1949]. We
implemented the Bron and Kerbosch algorithm [Bron and Kerbosch, 1973] to find all the
cliques in the network which were maximally complete sub-graphs of a size greater than
five. This method also provides an analysis of the overlapping structure of the cliques,
which gives information on the number of times a group of actors appears together among
the cliques generated. A hierarchical clustering is produced based on this information. This
Hierarchical Clustering of Overlapped Matrix is a series of nested partitions of overlapped
cliques that gives an overview as to which operators are clustered together at each level of
overlapped clique membership and which operators are in isolation [Johnson, 1967].
We used the weak favourite partnership matrix to generate the results as this matrix enables
us to cover a wider perspective on the whole network, allowing us to observe whether there
are other means by which operators are closely tied together each other. For our research
purpose, we chose the operators belonging to the three clusters at the level of 24 clique
memberships. At that level, we were able to avoid most of the insignificant cliques while
retaining the significantly larger clique subgroups. This measure is important as a
dependent variable, because it gives an indication as to how operators are grouped together
in the network in terms of the number of other operators with which they share direct ties.
At the level of 24 cliques memberships, we observed that there was a strong clique (clique
1) comprising of nine operators, all of whom were members of the Vodafone Group and its
Partner Network. There is another cluster (clique 2) which was made up of a total of 23
operators including operators such as MobileOne from Singapore and Singtel Optus from
Australia. A third smaller clique was also identified under UCINET VI program. However,
we later removed it from regression model due to insufficient financial data to support
further analysis.
- 13 -
Figure 3: Cliques of Mobile Operators - Graphical Representation of the Hierarchical
Clustering of Overlapped Matrix
4.3 Statistical Analysis
With the dependent variables generated, the next step of our analysis was to test the impact
of various factors on a carrier’s position in the relationship network (i.e, degree centrality),
and to what extend these factors have in an influence on the carrier’s clique membership.
The techniques used to investigate these questions are regression analysis and Multinomial
Logit Regression, respectively. The factors that are tested for their influence on the
structure of the partnership network are derived from the theoretical discussion and include
- 14 -
population size, GDP, GDP growth, number of international travellers, exports, imports,
carrier revenues and market share, and information about the carriers’ membership in an
existing international mobile alliance.
4.4 Linear Regression Analysis
Using linear regression analysis, we attempted to find out if there exists a statistical
relationship between our country-specific and firm specific factors and the level of degree
centrality a mobile operator has in the strong favourite partnership network. Due to the lack
of data needed for some of the control variables, the sample size used for this analysis was
reduced to 58 operators. Also, including dummy variables that indicated the mobile
operator’s membership status in one of the six roaming alliances did not yield any
significant results. Alliance membership has no influence on degree centrality in the strong
favourite partnership network. We omitted the dummy variables, so as to increase our
degrees of freedom for the estimation.
In order to be able to test arguments of Hite and Hesterly [Hite and Hesterly, 2001] and
Burgers, Hill and Kim [Burgers et al, 1993] that small and large firms are not attractive or
do not have the incentive to engage in alliances, while intermediate firms are most likely to
engage in alliances, we squared the Revenue variable after deducting the average value.
Hence, Revenue2 will take on low values for intermediate firms and high values for small
and large firms. A negative coefficient on Revenue2 can then be interpreted as intermediate
sized firms tending to have high degree centrality in the strong favourite partnership
network. The results of the linear regression analysis are shown in Table 2.
The results show that at a 5% level of significance, Revenue2, export and import turned out
to be statistically significant in explaining level of degree centrality in the strong favoured
partnership network. All other explanatory variables turned out insignificant. The negative
significant coefficient on Revenue2 confirms the notion that intermediate sized firms
tending to have high degree centrality. Carriers in strong exporting nations tend to have
high degree centrality, whereas a high level of imports has a negative effect.
- 15 -
Regression Statistics
R Square
Adjusted R Square
Observations
Intercept
Revenue2 in m2 USD
Population in thousand
No. of international
Travellers in thousands
GDP Growth
GDP in m USD
Export in b USD
Import in b USD
Market share (%)
0.298751098
0.184261481
58
Coefficients
4.596955991
-7.78624E-09
2.38035E-05
Standard Error
t Stat
1.481302121 3.103321007
2.90699E-09 -2.67845733
6.80216E-05 0.349940773
-2.07342E-05
0.182848969
3.04314E-06
0.029181581
-0.052391336
0.8450906
3.6513E-05
-0.5678593
0.490646583 0.372669402
2.14573E-06 1.41823111
0.010460686 2.789643157
0.014583168 -3.59258951
0.516359736 1.636631482
Table 2: Results of Linear Regression Analysis
4.5 Multinomial Logit Regression (Polychotomous Regression)
The second type of statistical analysis assesses which factors influence the probability of
operators being in a certain clique as opposed to being in no clique. The model used to
conduct this analysis is a regression for categorical variables, also known as polychotomous
regression method. The dependent variable in this regression is a categorical variable that
takes on the value 1, if a carrier is member of clique 1; 2, if a carriers is member of clique
2; and 0, if the mobile carrier is not member of any clique. The actual values of the
dependent variable do not bear any meaning in the categorical regression method we used,
as the dependent variable is categorical.
We tested two different specifications. The first specification uses the categorical clique
membership variable as dependent variable and the same independent variables as the
linear regression model (that is, population size, GDP, GDP growth, number of
international travellers, exports, imports, carrier revenues and market share). The second
- 16 -
model is used to regress mobile alliance membership on clique membership. It is aimed at
analyzing whether existing mobile alliances in deed behave like strategic alliances in that
their members constitute networks of (weak) favourite partnerships. Table 3 shows the
result of the multinomial regression analysis for the first specification. The figures shown
under “clique 1” (“clique 2”) in Table 3 can be interpreted as signifying how the various
factors influence the probability of a mobile operator being in clique 1 (2), as opposed to
being in no clique at all. The tables show that only exports and imports have a significant
impact on the probability of membership in clique 1. None of the tested factors has an
impact on the probability of being in clique 2.
Table 4 shows the respective results for the second model specification. The only
statistically significant relationship in this specification turned out to be that the
membership in clique 1 is significantly related to the membership in the VODAFONE
group. In other words, there is a congruency between he VODAFONE group and clique 1.
The VODAFONE group constitutes the only mobile alliance group in which alliance
members tend to maintain favourite partnerships.
4.6 Interpretation of Results: Social Network Analysis, Linear Regression Analysis
and Multinomial Logit Regression Analysis
In the following, the main results of our analysis are collated and substantiated with
information on the existing alliances.
First, the signs of the coefficients on the import and export variables under the simple
regression analysis coincide with those in the Multinomial Logit Regression model.
Operators from countries that are more self-sufficient due to their higher export to import
ratio were able to gather a larger number of “favoured partners” (achieving a higher degree
centrality). The result may be due to the fact that the country is viewed as being more
commercially attractive due to its comparably healthy balance of trade. A higher density of
business travellers can be expected to travel out of these countries in order to close deals
and expand their export network. Hence, home carriers in major export countries become
more attractive as choices for “favoured partnerships”, due to the good prospects of demand
for roaming services.
- 17 -
Coefficient
Clique 1
Constant
Revenue in m USD
GDP Growth
GDP in m USD
Population in thsd
No. of international
Travellers in thsds
Export in b USD
Import in b USD
Market share
Clique 2
Constant
Revenue in m USD
GDP Growth
GDP in m USD
Population in thsd
No. of international
Travellers in thsds
Export in b USD
Import in b USD
Market share
Standard Error
Z - Value
-1.256841
-6.43E-08
-0.4748848
3.36E-06
0.000026
1.471628
6.72E-08
0.3791262
2.40E-06
0.0000616
-0.957
-0.957
-1.253
1.401
0.422
0.0000322
0.0000266
1.211
0.0285503
-0.0526513
0.7432566
0.0143829
0.024442
0.477089
1.985
-2.154
1.558
-2.184635
-4.19E-09
0.2115353
3.41E-06
-0.0000482
1.10521
3.07E-09
0.3725683
2.42E-06
0.0000608
-1.977
-1.365
0.568
1.407
-0.794
0.0000205
0.0000315
0.652
0.0268457
-0.0403785
0.6181257
0.0186545
0.024072
0.3848226
1.439
-1.677
1.606
Table 3: Results of Multinomial Regression (Model 1)
A second notable result is the negative coefficient on Revenue2, which can then be
interpreted as intermediate sized firms tending to have high degree centrality in the strong
favourite partnership network. As the theory predicts, intermediate sized mobile operators
are the most attractive roaming partners and therefore also possible the most attractive
alliance partners.
- 18 -
Coefficient
Standard Error
Z - Value
Clique 1
Constant
FreeMove
Starmap
AMI
FMC
BMA
Vodafone
-2.555614
-35.11231
-34.76509
-37.16133
-36.95047
-35.09092
3.536443
1.03785
5.01e+07
6.26e+07
7.08e+07
6.29e+07
1.06e+08
1.239139
-2.462
0.00
0.00
0.00
0.00
0.00
2.854
Clique 2
Constant
FreeMove
Starmap
AMI
FMC
BMA
Vodafone
-1.139568
-.4401402
-.4052154
-36.80504
-36.7532
0.4464207
0.7341028
0.5582025
0.9398791
1.206018
9.89e+07
7.76e+07
1.345953
1.070011
-2.04
-0.468
-0.336
0.00
0.00
0.332
0.686
Table 4: Results of Multinomial Regression (Model 2)
Third, we found that only in the case of the Vodafone Group roaming charges reflect the
attempt to strategically coordinate mobile carrier activities across countries. This result is in
line with the Vodafone Group’s objective to leverage its global leadership position and
market Vodafone as a single global brand while providing a seamless global offering across
the Group’s networks. The Groups’ determination to gain a stronghold in the European
market can be seen by the success of its Partner networks in providing the best reciprocated
prices to their members, resulting in a high degree centrality for most of its members. This
clearly shows that Vodafone group is on the way to achieving its corporate objective of
operating as a single global brand, whereas other carriers, despite of their newly formed
alliances, are clearly lagging behind in terms of coordination.
5. CONCLUSIONS
As roaming alliances become more prevalent in the mobile telecommunications industry, it
is important for mobile operators to better understand the dynamics of these alliances. This
study investigates firm-specific and country-specific factors that may influence the
- 19 -
formation of “favoured partnerships” in the mobile service industry and whether mobile
operators in existing alliance network act strategically as “favoured partners”.
Our analysis showed that country-specific factors such as population or GDP growth and
number of international travellers generally do no significantly influence the structure of
favoured partnership networks. In contrast, the size of the mobile operator (as measured in
revenue) does influence the nature of the relative position and operator hold in the network
of international roaming agreements. Other country-specific factors such as import and
export figures were also found to be significant to the attractiveness of an operator as a
strong favourite partner for roaming services.
Our results also show that only Vodafone Group and its Partner Network showed typical
“favoured partner” behaviour within its alliance. Vodafone achieved this through its
consistent application of its networking strategy and uniform distribution of the “alliance
benefits” without discrimination within its networks. This resulted in a high degree of
reciprocation of best prices within the group, making the “Vodafone Group and its Partner
Network” networking strategy more effective and successful as compared to other existing
alliance strategies. The other alliances, such as FreeMove, had a relatively low rate of
reciprocation due to inconsistent pricing within their network. For instance, their lowest
prices were not restricted to alliance members, and were given on a non-discriminatory
basis. This resulted in mobile alliance membership having low significance in explanatory
power with respect to the degree centrality of the telecommunication operators. This allows
us to conclude that significant improvement can be made with respect to the strength of
international cooperation between mobile operators.
Existing alliances tend to emphasize value-added services as a benefit derived from alliance
formation and place little regard on the importance of simple roaming prices as an means to
create end-user benefits. Building upon this study, further research can be carried out to
study the demand for these value-added services, and whether these value-added services
should actually be the sole focus of future alliance formation.
Also, assuming that a rational individual would go for the lowest price, ceteris paribus,
why are mobile operators who charge premium prices for its services as compared to their
competitors still able to gain a substantial market share in the mobile roaming market?
- 20 -
Further research may look at the nature of the services offered in such markets and
conditions under which consumers are willing to pay premium prices. Such further studies
may help to understand consumer’s needs better, and uncover new pricing strategies.
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- 25 -
APPENDIX B: Operators included in this study
COUNTRY
CODE OPERATOR'S NAME
Australia
AUS1
AUS2
AUS3
AUS4
Hutchison 3G Australia Pty Limited
Singtel Optus Limited (YES OPTUS) (Cable & Wireless)
Telstra Corporation Limited (Telstra MobileNet)
Vodafone Pacific Limited
Austria
AUT1
AUT2
AUT3
AUT4
AUT5
Hutchison 3G Austria GmbH (3 AT)
Mobilkom Austria AG & Co KG (A1)
ONE GMBH (Connect)
T-Mobile Austria GmbH (Max)
Tele.ring Telekom Service GmbH
Belgium
BEL1
BEL2
BEL3
BASE NV/SA (KPN Orange)
Belgacom Mobile (PROXIMUS)
Mobistar S.A.
Canada
CAN1
CAN2
Microcell Telecommunications Inc (Fido)
Rogers Wireless
Czech Republic
CZE1
CZE2
CZE3
Cesky Mobil a.s (Oskar Mobil)
Eurotel Praha
T-Mobile Czech Republic a.s. (Radiomobil)
Denmark
DEN1
DEN2
DEN3
DEN4
DEN5
Hi3G Denmark ApS (3 DK)
Orange A/S
Sonofon
TDC Mobil A/S (Tele Danmark)
Telia A/S Denmark (TELIA DK)
Finland
FIN1
FIN2
Alands Mobiltelefon Ab (Altel)
Finnet Networks Ltd (Finnish 2G) (Suomen 2G)
FIN3
FIN4
Radiolinja Origo Oy (elisa)
Sonera Mobile Networks Limited (Witnet)
France
FRA1
FRA2
FRA3
Bouygues Telecom
Orange France (France Telco)
SFR
Germany
GER1
GER2
GER3
GER4
E-Plus Mobilfunk GmbH
O2 (Germany) GmbH & Co. OHG (VIAG)
T-Mobile Deutschland GmbH (D1) (DeTe Mobilnet)
Vodafone D2 GmbH (Mannesmann)
- 26 -
COUNTRY
CODE OPERATOR'S NAME
Greece
GRE1
GRE2
GRE3
GRE4
COSMOTE
Info Quest - Commercial & Industrial SA (Q-TELECOM)
Stet Hellas Telecommunications S.A. (TIM) (Telestet)
Vodafone-Panafon
Hongkong
HON1
HON2
HON3
HON4
HON5
HON6
China Resources Peoples Telephone Company Ltd
Hong Kong CSL Limited
Hutchison Telecom (HK) Ltd (3) (Orange) (Telecom
New World PCS (New World Mobility)
SmarTone Mobile Communications Limited
Sunday Communications Ltd (Mandarin)
Hungary
HUN1
HUN2
HUN3
Pannon GSM Telecommunications
T-Mobile Hungary Telecommunications Co. Ltd (Westel)
Vodafone Hungary Ltd
Iceland
ICE1
ICE2
ICE3
Iceland Telecom Ltd (LANDSSIMINN) (Siminn)
IMC Island ehf (Viking wireless)
Og Fjarskipti Hf (Og Vodafone) (Islandssimi) (TAL)
Ireland
IRE1
IRE2
IRE3
METEOR
O2 Communications (Ireland) Ltd (East Digifone)
Vodafone Ireland Plc (Eircell)
Italy
ITA1
ITA2
ITA3
ITA4
H3G
Telecom Italia Mobile (TIM)
Vodafone Omnitel N.V.
Wind Telecomunicazioni SpA
Japan
JAP1
JAP2
NTT DoCoMo, Inc
Vodafone K.K. (J-Phone)
South Korea
KOR1
KOR2
KT Freetel Co., Ltd (KT ICOM)
SK Telecom
Luxembourg
LUX1
LUX2
LUX3
P+T Luxembourg (LUXGSM)
Tango S.A (Tele 2 ab)
VOXmobile S.A. (VOX.mobile)
Malaysia
MAL1
MAL2
MAL3
Celcom (Malaysia) Sdn Bhd (CELCOM GSM) (TMTouch)
Digi Telecommunications Sdn Bhd
Maxis Communications Berhad (MMS & MB) (Malaysian
Mexico
MEX1
MEX2
Pegaso Comunicaciones y Sistemas, S.A. De C.V
Radiomovil Dipsa SA de CV (TELCEL) (TELCEL GSM)
- 27 -
COUNTRY
CODE OPERATOR'S NAME
Netherlands
NET1
NET2
NET3
NET4
NET5
KPN Mobile The Netherlands BV
Orange Nederland N.V. (Dutchtone)
T-Mobile Netherlands (T-Mobile NL) (Ben)
TELFORT B.V. (O2)
Vodafone Libertel N.V
New Zealand
NEW1
Vodafone Mobile NZ Limited
Norway
NOR1
NOR2
NOR3
Maritime Communications Partner AS
NETCOM AS
Telenor Mobil
Poland
POL1
POL2
POL3
Polkomtel S.A (PLUS GSM)
Polska Telefonia Cyfrowa (Era)
PTK Centertel (IDEA)
Portugal
POR1
POR2
POR3
Optimus Telecomunicacoes, S.A
Telecomunicacoes Moveis Nacionais S.A (TMN) (Oni Way)
Vodafone Portugal (Telcel)
Singapore
SIN1
SIN2
SIN3
Singtel
MobileOne
Starhub
Slovakia
SLO1
SLO2
EuroTel Bratislava as (EUROTEL GSM)
Orange Slovensko a.s (Orange SK)(Globtel)
Spain
SPA1
SPA2
SPA3
Retevision Movil S.A (AMENA)
Telefonica Moviles Espana S.A. (MOVISTAR)
Vodafone Espana S.A. (Airtel)
Sweden
SWE1
SWE2
SWE3
SWE4
SWE5
HI3G Access AB (3)
Swefour AB (Spring Mobile)
Tele 2 AB (COMVIQ)
TeliaSonera Mobile (TELIA MOBILE)
Vodafone Sverige AB (Europolitan)
Switzerland
SWI1
SWI2
SWI3
Orange Communications S.A
Swisscom Mobile Ltd (Natel)
TDC Switzerland AG (Sunrise) (DiAX)
Taiwan
TAI1
TAI2
TAI3
TAI4
TAI5
Chunghwa Telecom (LDTA GSM)
Far EasTone Telecommunications
KG Telecom (Tuntex)
Mobitai Communications Corp.
Taiwan Cellular Corporation (TCC)
- 28 -
COUNTRY
CODE OPERATOR'S NAME
Taiwan
TAI6
TAI7
TransAsia Telecommunications
VIBO Telecom Inc (T3G)
Thailand
THA1
THA2
THA3
THA4
THA5
ACT Mobile Company, Limited
Advanced Info Service PLC (AIS GSM)
Digital Phone Co Ltd
TA Orange Company Ltd
Total Access Communications Co (DTAC)
Turkey
TUR1
TUR2
TUR3
TUR4
Aycell Haberlesme ve Pazarlama Hizmetleri A.S.
TELSIM Mobil Telekomuniksyon Hiz.A.S (TELSIM GSM)
TT &TIM iletisim Hizmetleri (ARIA) (Turk Telecom)
Turkcell Iletisim Hizmetleri A.S.
United
Kingdom
UK1
UK2
UK3
UK4
UK5
Hutchison 3G UK Ltd (3)
O2 (UK) Limited (Cellnet)
Orange PCS Ltd
T-Mobile (UK) Limited (One to One)
Vodafone Ltd
United States
USA1
USA2
USA3
AT&T
T-mobile
Cingular Wireless (Bell South) (Pacific Bell)
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APPENDIX B: BACKGROUND ON MOBILE OPERATOR ALLIANCES
B.1 Fixed Mobile Convergence Alliance (FMCA)
The FMCA started off with six leading carriers; United Kingdom's British Telecom, Brasil
Telecom, Korea Telecom, Nippon Telegraph and Telephone, Rogers Wireless and
Swisscom in July 2004. Latest additions to the alliance include Eurotel Praha and Telstra
Corporation and this has expanded their joint base to 122 million fixed network users and
23 million mobile subscribers. Goals of the FMCA include providing cost-efficient
converged fixed-mobile line services to consumers; influencing and promoting fixedmobile convergence, thereby stimulating competition which could lead to high quality,
low-priced phones. It also aims to share information and expertise on converged fixedmobile services, while working jointly on common technology standards together with
major standards bodies. The FMCA is differentiating in terms of the level of convenience
provided to customers as their technology enables customers to connect multiple hand-held
devices to all of their services via Bluetooth.
B.2 Vodafone Group and Partner Network
The Vodafone Group was formed when Vodafone expanded beyond the United Kingdoms
in the 1990s. The Group’s objective to increase foreign direct investment when appropriate
so as to avoid alliances and joint ventures wherever possible has resulted in its current
stable of subsidiaries and associates spanning across twenty six countries and five
continents. To further gain a stronghold in the European market, the Group has also set up
their own Partner Network consisting of mostly European operators. The first strategy,
coupled with the Group’s Partner Network, which is made up of leading mobile operators
around the world, allows it to achieve its other objectives of securing a global presence in
terms of geographical and brand reach, and to create a worldwide mobile network which
allows the provision of a seamless global offering across the Group’s networks. It also
allows the Group to derive additional revenue from fees and visitor roaming from its
Partner Network without incurring equity investments while allowing access to other
international telecommunication markets, including those of the Group’s partners, resulting
in a total subscriber base of 112.5 million. Another objective of the Group was to create an
additional competitive advantage by leveraging its global leadership position and by
marketing Vodafone as a single global brand.
B.3 Asia Mobility Initiative (AMI)
AMI, the first alliance initiative in Asia, is a non exclusive group with seven leading mobile
operators as its members. It was formed on 7th April 2003 by its five founding members
CSL (Hong Kong), Maxis (Malaysia), MobileOne (Singapore), Smart (Philippines) and
Telstra (Australia). By late 2003, two more members were added to the list namely DTAC
(Thailand) and CTM (Macau), increasing their subscriber base to 31.4 million. AMI
focuses on providing consumers with simpler standardized access and an improved
experience in mobile data services through greater inter-operability across the networks,
new platforms and jointly produced devices. AMI has constantly reinforced their regional
competitive advantage by competing in the niche market of entertainment services,
delivering quality services such as games for the customers.
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B.4 Starmap Mobile Alliance
Starmap Mobile Alliance, initially known as Mobile Alliance, was formed on 1st October
2003 by nine founding leading European mobile operators, Amena (Spain), O2 (Germany,
UK and Ireland), One (Austria), Pannon GSM (Hungary), Sunrise (Switzerland), Telenor
Mobil (Norway) and Wind (Italy). They aim to provide a “Feel at home whenever you go”
seamless access to voice and data services for business and consumer customers across
Europe. The group also endeavors to provide innovative, attractive and simpler services to
their customers through collaboration throughout the supply chain. By late 2004, two more
members Eurotel (Czech Republic) and Sonofon (Denmark) were added to the list
expanding their subscriber base to 53 million. Constant provision of new experiences such
as the recently launched multi-country corporate service for consumers has been crucial as
a competitive advantage.
B.5 FreeMove Alliance
FreeMove Alliance was formed in 2003 by four leading telecommunication operators, TMobile, Telefonica Moviles, Telecom Italia Mobile and Orange. It reaches almost 230
million customers worldwide. They aim to deliver an enhanced service, providing a
seamless experience to their customers whether at home or abroad. For example, customers
are ensured access to familiar home services like voicemail while abroad. Members are
improving global competitiveness by cooperating to increase operational efficiency,
reaping economies of scale in R&D and bulk purchase, and improving the speed and
quality of its service. A significant draw of FreeMove is its simple and predictable roaming
pricing plans which allows for greater cost transparency. In addition, FreeMove has also
expanded its alliance network to further grow in strength and build upon its current
achievements.
B.6 Bridge Mobile Alliance
The Bridge Mobile Alliance represents the largest alliance among telecommunication
operators in the Asia Pacific. Formed in late 2004, by 7 Telco’s; Bharti (India), Globe
Telecom (Philippines), Maxis (Malaysia), Optus (Australia), SingTel (Singapore), Taiwan
Cellular Corporation (Taiwan) and Telkomsel (Indonesia), the group has a combined
customer base of more than 56 million with further regional expansion in mind. Although
this alliance is not likely to result in lower charges for customers, it is likely to boost
roaming revenue of the seven operators as the availability of enhanced services from
alliance members is likely to encourage customers to use the networks within the alliance.
Alliance members will reap economies of scale as the operators jointly invest in
infrastructure and technological development. Bridge Mobile also aims to develop new
products and services and create core competencies for their network members.
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