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INTERNATIONAL SCHOOL OF MANAGEMENT
MASTER OF SCIENCE PROGRAMME
Nerijus Dumbrava
Student ID: 0786324
MASTER'S THESIS
TITLE OF THE THESIS
Supervisor
Nerijus Pačėsa 2005 _ _
Supervisor
Lars Thue 2005 _ _
Reviewer
2005 _ _
Reviewer
2005 _ _
VILNIUS, 2005
C O N T E N T S List of Figures 2
List of Tables 3
Introduction 4
1. Overview of 1G, 2G, 2.5G, 3G mobile communication systems 7
1.1. First generation (1G) 7
1.2. Second generation (2G) 9
1.3. Intermediary generation (2.5G) 11
1.4 Third generation (3G) 11
1.5 1G, 2G, 2.5G, 3G compared 13
1.6 1G, 2G, 3G in Lithuania 14
2. Internal - structural factors influencing growth 15
2.1 Network effect 15
2.1.1 Michael L. Katz and Carl Shapiro 15
2.1.2 Stan Liebowitz and Steve Margolis 20
2.1.3 Other authors 21
2.2 Diffusion of innovations 23
2.2.1 Everett M. Rogers 23
2.2.2 Bass model 25
3. External – regulation factors influencing growth 29
3.1 Methods of allocation of scarce resources 29
3.2 Role of regulation 32
3.3 Alternative approach 34
4. Empirical analysis 37
4.1 Example of empirical research 37
4.1.1 Massini’s research 37
4.1.2 Botelho research 40
4.2 Empirical data analysis 43
4.2.1 Correlation analysis 45
4.2.2 Regression analysis 48
4.2.3 Curve estimation 53
Conclusion 58
References 60
1
L I S T O F F I G U R E S
Figure 4.1 Scatter diagram for mobile phone subscribers and GDP
Figure 4.2 Scatter diagram for mobile phone subscribers and average income.
Figure 4.3 Scatter diagram for mobile phone subscribers and fixed line subscribers
Figure 4.4 Curve fit analysis for Linear, Quadratic, Cubic and Power functions
Figure 4.5 Curve fit analysis for Compound, S, Growth, Exponential and Logistics
functions
2
L I S T O F T A B L E S
Table 1.1 Table 1.1 features of 1.5 1G, 2G, 2.5G, 3G mobile communication generation systems
Table 4.1 Cumulative data of mobile phone and fixed line subscribers.
Table 4.2. Economical variables
Table 4.3 Correlation. Number of mobile phone subscribers and GDP.
Table 4.4 Correlation. Number of mobile phone subscribers and average income
Table 4.5 Correlation. Number of mobile phone subscribers and fixed subscribers
Table 4.6 Regression coefficients. Mobile phone subscribers and fixed line subscribers
Table 4.7 Regression characteristics. Mobile phone subscribers and fixed line subscribers
Table 4.8 Regression coefficients. Mobile phone subscribers and GDP
Table 4.9 Regression characteristics. Mobile phone subscribers and GDP.
Table 4.10 Regression coefficients. Mobile phone subscribers and average income
Table 4.11 Regression characteristics. Mobile phone subscribers and average income.
Table 4.12 Multiple regression coefficients. Mobile phone subscribers, fixed line subscribers
and GDP.
Table 4.13 Multiple regression characteristics. Mobile phone subscribers, fixed line
subscribers and GDP.
Table 4.14 .Multiple regression coefficients. Mobile phone subscribers, fixed line
subscribers and average income.
Table 4.15 Multiple regression characteristics. Mobile phone subscribers, fixed line
subscribers and average income.
Table 4.16 Time series functions results.
Table 4.17 Estimation results of the exponential model
3
INTRODUCTION Mobile communication is one of the most exciting technological developments of last decade.
No segment of any other industry has seen growth that happened in mobile communications.
From relatively modest start, the last 15 years where an explosion in the number of mobile
communications users. Starting from early introduction in 1990, number mobile telephone
subscription has doubled globally every 20 months. Starting from an 11 million subscriber
and an average penetration of 1% at 1990, the mobile communications industry now provides
services to 1,404 billion subscribers (International Telecommunication Union, 2003). As the
number of mobile subscribers in some countries is already overtaking fixed line subscribers
and reaching saturation level higher than 100%, it looks that mobile communication is
becoming primary mean of voice communication transmission However it appears, that
growth is likely to continue in the future taking even more interesting forms after the
introduction of technically advanced mobile cellular networks. This master thesis is intended
to examine the growth of mobile telephony and the factors that affect this growth.
Varying explanations of the growth factors can be found in the vast research literature of the
subject. Gary Madden, Grant Coble-Neal, Brian Dalzell (2004) suggest that one of the
reasons of such impressive growth over last decade could be market situation itself, because
the situation in mobile telecommunication sector from the early start was very different than
in the sector of traditional fixed line telecommunication. Authors remark that “Entry of
mobile providers into former monopoly markets ensured the evolution of a more competitive
environment.” From the very first mobile operators where operating in very competitive
environment, because usually in most countries at least two service providers where issued
with the licenses. That’s is why operators had to employ the strategies based on high
customers satisfaction: developing pricing packages to differentiate themselves from their
competitors, isolate market segments and target specific customer groups and geographic
regions
Jha and Majumdar (1999) have find out that mobile telephony penetration is varying
considerably amongst different countries depending to their gross domestic product (GDP).
Authors insist that bigger economical success translates into an superior demand for mobile
telecommunications services.
4
Gruber, Verboven (2001) notice that growth in mobile telephony might be affected by such
regulatory influences as the timing and number of licenses issued the method which is used to
grant the licenses, timing and manner of technological standardization.
However Erik Bohlin, Stanford L. Levin, Nakil Sung in the editorial of Special Issue on
Growth in Mobile Communications of Telecommunications Policy remark that “As is widely
recognized, accurately predicting the evolution of demand for new applications of technology
has eluded experts again and again. The history of mobile communications has been
characterized by underestimation of overall demand, overestimation of the potential of certain
applications (such as WAP), and the failure to foresee the popularity of others (such as
SMS).”
But the authors also add that complicatedness of forecasting does not necessarily mean that
various interest parties in the mobile communications field cannot impact developments by
creating environment for expansion, meaning that it main concern should be put on
identifying and removing concrete barriers to continues growth. Balance should be found
between the requirements of consumers and demands businesses, the availability of
infrastructure and development of services, the financial equilibrium of operators, equipment
developers and other players in the mobile wireless area and the control employed by various
regulatory bodies.
Development of technologies ads one more interesting feature to the picture – services of data
transmission over mobile devices under 2.5G and 3G systems are already offered in some
markets. Already today there are many prediction and different opinions how these systems
should work, how their services should be priced and how consumers will react. Author of
master thesis also intends to touch this topic from theoretical where it is applicable,
addressing the challenges that will appear then the technological generation will change.
The thesis is organized as follow:
Section 1 is addressing technical issues related to the evolution of mobile communication
networks. It is intended to provide information about standards, generations, and timeline of
the evolution until current date and brief overview of mobile communication networks
development in Lithuania.
5
Section 2 provides discusses of relevant theoretical concepts from theory of economics of
innovations literature. To aid this discussion two most relevant subjects where chosen and
will be addressed:
1. Network effect
2. Diffusion of technological innovation
Mentioned concepts are studied having in mind their impact on researched topic thus linking
some of the researcher’s theoretical findings with practical examples from mobile
communication industry. Also these findings and concepts will be used in empirical part of
the research.
Section 3 will be used to address the role of state regulation by reviewing relevant researches
mainly about auctions of frequency distribution.
Section 4 concludes the paper by performing empirical research of growth factors of
Lithuanian mobile communication market. Existing researches on similar topic will be
reviewed in this section as well.
General focus research question: How has mobile communication industry developed to
current stage? Which factors and how influenced growth of mobile subscribers?
Author assumes that following research questions with corresponding objectives should be
addressed in his research:
Research question Research objective
1. How has mobile communication
developed till now?
2. What implications, concepts and
ideas does literature provide about
the possible reasons for growth
mobile communication?
3. What is the current role of market
regulating bodies in fostering or
decreasing the growth?
1. To overview creation, main
principles and development of
GSM standard
2. Examine the theoretical
concepts of network effect and
diffusion of technological
innovation and find possible
links with the growth.
3. Overview the researches
examining role of regulation and
methods of regulation.
6
4. How main factors driving mobile
communication growth can be
captured?
4. Compare quantitative analysis
results.
7
1. Overview of 1G, 2G, 2.5G, 3G mobile communication systems
Before deeply analyzing driving forces of mobile communication development and its current
state, it is appropriate to review mobile communication technical development timeline, not
forgetting first and second generation systems. Like in most information technologies,
advances in mobile communications occur through a process of gradual evolutionary
development and the “occasional quantum-leap forward” (Clint Smith, 2001) periods. This
development is also characterized by term “hype cycle” by Gartner researchers1. That’s why it
is important to review development of mobile communication systems and try to observe
patterns of the possible past hype cycles, which might be a useful tool for the predictions and
forecasts about upcoming generation of mobile communication systems. Also before
analyzing such a complex and dynamic industry as mobile communication, it is important to
have some knowledge about basic technical details to better understand what is the
environment of the industry in which decisions are made.
1.1. First generation (1G) Most of researchers, analyzing development of mobile communications systems, mention
USA as a place, where mobile communication technologies where born. First successful
implementation of the trial system was conducted in Chicago in 1978. The system was based
on technology called Advanced Mobile Phone Service (AMPS) and was operating at 800MHz
frequency. Commercial launch of this system was delayed and took place only in 1983.
However other countries were also making significant progress. Japan has launched a
commercial mobile communication system in 1979. System was based on the same Advanced
Mobile Phone Service platform which was tried in the USA in 1979. In 1981 the first
European mobile communication system was launched in Norway, Sweden, Denmark, and
Finland simultaneously. This system used a technology known as Nordic Mobile Telephony
(NMT) and was operating in the 450MHz band and became known as NMT-450. Later
version of the system was working in 900MHz band and was named NMT-900. System was
widely recognized as a successful project and later was installed throughout Europe, Asia, and
Australia.
1 http://www4.gartner.com/pages/story.php.id.8795.s.8.jsp
8
Britain introduced at that time has chosen to adopt its own technology which was called Total
Access Communications System (TACS) and took place in 1985. Actually TACS was
upgraded version of AMPS, which was installed in USA and Japan.
In a few years time many other developed countries followed along joining the growing
community of mobile communications users, and soon mobile communications services
spread across the continents. During that time several other technologies were developed, but
AMPS, TACS NMT450 and NMT900 were the most successful and most widely adapted
technologies. Some of these systems are still in service even today. As (Collins, 2002) writes
“First-generation systems experienced success far greater than anyone had expected.”
But every success has its own limits. As the number of subscribers grew rapidly, they
exceeded potential capabilities of the systems, especially in the highly populated areas. Due to
specific architecture of the systems, it was already impossible to assure good quality of the
service and it was clear that actions should be taken immediately. This lead to the
development of second generation systems (2G).
1.2. Second generation (2G) Year 1982 could be called a virtual starting point for the development of second generation
system, because in this year the Conference on European Posts and Telecommunications
(CEPT) embarked on developing new generation mobile communication system by
establishing a group called Group Spéciale Mobile (GSM, which later become an acronym for
second generation standard). After conducting an early technical work of the new digital
standard, work was overtaken by the newly created European Telecommunications Standards
Institute (ETSI) in 1989. ETSI finalized the first set of technical specifications giving the
same name of an earlier standard developer’s group– GSM.
The first functioning GSM network was set up in 1991, followed by several more launched in
1992. Also international roaming between the various networks soon followed. GSM was
regarded as hugely successful project and within few years almost all countries in Europe
between 1992 and 1996 set up GSM service, followed by countries in other continents. It
became evident that GSM will be more than just a European project - it was fast becoming
global, by changing the meaning of GSM letters to “Global System for Mobile
communications”
9
Consequently, the letters GSM have taken on a new meaning—Global System for Mobile
communications.
After few years one more important enhancement followed. Originally, GSM was designed
to operate to use 900MHz band, but to increase network capacity 1800MHz band was added
to the standard, called DCS1800 since which function simultaneously with the old frequency.
New sets of handsets where developed to support both frequencies. Nowadays most handsets
support also 1900 MHz frequency which is used for GSM in North America.
(Collins, 2002) mentions following benefits of 2G systems over 1G:
1. Increased capacity over analog technology
2. Reduced capital infrastructure costs
3. Reduced the capital per subscriber cost
4. Reduced cellular fraud
5. Improved features
6. Encryption
Most of the mentioned points directly benefit operator of the wireless system, but benefits
which users receive can also be observed:
1. Lower service cost (due to reduced capital infrastructure costs and increased capacity
of the network)
2. Better voice quality, higher success of connection
3. Additional features, new services (SMS, international roaming, sim card options, data
transmission)
Many reasons for the rapid growth of second generation mobile communication (most of
these reasons will be addressed later in this thesis), but one important detail should be
mentioned above all of them. GSM was highly technically advanced technology (and still is),
because system design was made from the scratch without providing backward compatibility
with existing analogue systems. This results in following:
1. System offers much mores advanced technological features, and is not connected to
previous generation analogue technology by any means.
2. Network operators are encouraged to build new networks as fast as possible because
there is no backward compatibility.
1.3. Intermediary generation (2.5G) 2.5G could be called an intermediate mobile communication generation linking existing 2G
with 3G which is still under development. 2.5G basically is the method by which existing
cellular operators are migrating into the next generation wireless technology, which is
10
extensively specified the International Mobile Telecommunications-2000 (IMT-2000)
specification. For the implementation of 2.5G, there is no need to build totally new network,
because services are provided by upgrading current 2G equipment. This means that 2.5G
offers a backward compatibility which is extremely important having in mind huge
investment which will be required to set up 3G networks.
Following platforms are currently used in 2.5G systems:
• General Packet Radio Service (GPRS)/ High Speed Circuit Switched
Data (HSCSD)
• Enhanced Data Rates for Global Evolution (EDGE)
2.5G gives the wireless operators a possibility to provide digital high speed data transmission
services prior to the availability of 3G platforms. Providing 2.5 services, before 3G is very
beneficial to the operator:
1. They can research customers needs
2. They can develop various pricing schemes
3. They have time to “educate” the customer about services which high data transmission
technology provides
On the other side, (Collins, 2002) mentions following challenges for the operators engaging in
providing 2.5G services:
1. No one specific standard chosen for transition.
2. The overlay approach
3. The introduction of packet data services
4. The new user devices required
5. New modifications to existing infrastructure
1.4 Third generation (3G) The demand for the next generation mobile communications technology became observable
during the period of rapid development and usage of networking technologies in the 90’s,
especially internet. Users soon realized that presence of a constant high speed connection
enables them to perform variety of different activities which prior where hardly possible or
even imaginative. Adding feature of mobility to the high speed access point would widen the
possibilities of high speed data transmission services even more.
11
Recognizing that fact, The International Telecommunications Union (ITU) in the 1990’s
launch the initiative called Future Public Land Mobile Telecommunications Systems
(FPLMTS) which was meant to prepare recommendation for the next generation mobile
communication systems. In 1997 ITU presented the recommendations under the name
“International Mobile Telecommunications—2000” (IMT-2000) which gave general direction
for the development of 3G mobile communication systems.
The IMT-2000 recommendations were intended to be unifying specification, enabling mobile
high-speed data services using one or several radio channels based fixed network for
providing the services under following conditions:
1. Global standard
2. Compatibility of services within IMT-2000 and other fixed networks
3. High quality
4. Worldwide common frequency band
5. Small terminals for worldwide use
6. Worldwide roaming capability
7. Multimedia application services and terminals
8. Flexibility for evolution to the next generation of wireless systems
9. 2Ghz operating band
10. High-speed packet data rates:
a. 2 Mbps for fixed environment
b. 384 Mbps for pedestrian
c. 144 Kbps for vehicular traffic
As these where only general recommendation, ITU announced that it is open for the
submission of technical 3G implementation proposals. After these proposal where submitted,
5 technologies for terrestrial service where announced:
• Wideband CDMA (WCDMA)
• CDMA 2000 (an evolution of IS-95 CDMA)
• TD-SCDMA (time division-synchronous CDMA)
• UWC-136 (an evolution of IS-136)
• DECT
12
1.5 1G, 2G, 2.5G, 3G compared
Following table summarizes all mobile communication generation systems features,
standards, specifications.
Table 1.1 features of 1.5 1G, 2G, 2.5G, 3G mobile communication generation systems
Generation Standards Features Speed Band
1G AMPS Analogue voice service,
No data service
300MHz -
600MHz
2G CDMA,TDMA,
GSM,PDC
Digital voice service;
Low speed data
transmission;
Enhanced calling
features, caller ID;
Voice mail;
Short messages;
Global roaming
9.6K - 14.4K
bit/sec
600MHz -
1.8GHz
2.5G GPRS, EDGE,
HSCSD,
Phone calls/fax;
Send/receive medium
size email messages;
Web browsing;
Navigation/maps;
New updates;
Multimedia messages
64-144kb/sec
3G
WCDMA,
CDMA2000,
TD-SCDMA,
UWC-136,
DECT
Send/receive large email
messages;
High-speed Web;
Navigation/maps;
Videoconferencing;
TV streaming;
Electronic agenda
144kb/sec-
2mb/sec
1.5GHz -
3GHz
13
1.6 1G, 2G, 3G in Lithuania
First 1G generation mobile communication NMT-450 licence in Lithuania was issued
1992.06.04 by Lithuanian Communication and Transportation Ministry to the joint stock
company of Lithaunia and Luxemburg „Comliet“. Licence was issiued for 10 years period for
the price 884,000LTL. For the next 3 yeras „Comliet“ was leading mobile communication
operator in Lithuania with network covering about 80% of state territory. However with the
introduction of GSM technology it‘s share started to decilne significantly and in year 2000
„Comliet“ was acuired by local fixed telephony monopolis „Lietuvos Telekomas“. Currently
„Lietuvos Telekomas“ operates „Comliet“ as a fixed line telephony substitution provider,
where due to infavourable conditions is not possible to have traditional fixed line telephony.
First 2G mobile communication GSM DCS 900 licence in Lithuania was issued 1994.10.25
by Lithuanian Communication and Transportation Ministry to the Joint Stock Company
„Litcom“ which was later renamed to „Omnitel“. Currently „Omnitel“ is leadind mobile
communication network operator.
Second 2G mobile communication GSM DCS 900 licence in Lithuania was issued
1995.05.09 to the Joint Stock Company „Mobilios telekomunikacijos“ which was later
renamed to „Bitė GSM“. Currently „Bitė GSM“ is one of the 3 mobile communication
operators in Lithuania with market share...
Due to vast network expansion and excessive number of subscribers growth „Omnitel“ and
„Bite GSM“ in 1997 requested second GSM DCS 900 licence. The where where issued with
the second licence on 1997.10.31. Each of the licences was issued for 10 years with fee
884,000LTL.
1998.02.23 proposed a tender (based on „beauty contest“ model which wil be examined with
more details later in the master thesis) for GSM DCS-1800 licence.1998.09.23 thee winners
where anounced: „Omnitel“ , „Bite GSM“ , „Levi and Kuto“. Joint Stock Company „Levi and
Kuto“ was later renamed to Tele-2. Tele-2 currently is second bigest mobile communication
network operator in Lithuania with market share.. 2000.12.29 „Tele-2“ was issued with the
DSC 900 licence. All mentioned licences tradionaly issued for 10 years period for 884,00LTL
fee. 3G licences in Lithuania are currently not issued.
14
2. Internal - structural factors influencing growth
In the following section various sources of network effect literature are summarized. For the
master thesis two main groups of authors where chosen: traditionalists, which started
developing network effect concepts in the 5th – 6th decade of the 20th centaury and few
relatively young and modern authors from the 9th decade of 20th centaury who are currently
trying to renew and supplement traditional concepts.
2.1 Network effect
2.1.1 Michael L. Katz and Carl Shapiro
It seems rather logical to start analyzing and reviewing literature on network effect from the
mostly quoted paper of this subject – “Network externalities, competition, and compatibility”
by Michael L. Katz and Carl Shapiro. This was one of the first papers which described
network effect in a fashion which is commonly used today. Katz & Shapiro (1985) observe
that “there are many products for which the utility that a user derives from consumption of the
good increases with the number of other agent consuming that good”. Authors of the paper
were one of the first to give reason for positive consumption externalities arising in the
network. In the following section author of the master thesis intends to relate these reasons
with the main topic of the thesis:
1) Direct physical effect which increasing number of purchasers has on the quality of
the product.
Quality in this case means, that as more agents join, more convenient and applicable service
becomes (in case of mobile telephony, as number of mobile phone users increases, the
incentive for new potential users to join also increases).
2) Indirect effect arising from hardware – software paradigm.
As number of mobile phone subscribers increases, network operators create and provide
more and more additional services (for example SMS, fax, email, MMS) which are also
incentive to join. In case of 2.5G and 3G these services can be provided not only by network
operators, but by external service providers, which will mean even more additional and
attractive services and applications.
3) Post purchase service development.
15
As current mobile communication networks in most countries do have full coverage of the
area, post purchase services are usually highly developed for most of the network operators.
Some post purchase services, such as handsets or prepaid SIM cards distribution, are provided
by other retailers which are not connected to network operators. This also has big impact as
positive network externality for mobile communication market.
Also Katz & Shapiro (1985) state that “For communication networks, the question is one of
whether consumer using one firm’s facilities can contact consumers who subscribe to the
services of other firms”. This is applicable to mobile communication networks, because
current networks are interconnected nationwide and worldwide with global roaming system.
But also we should not forget that international calls are still relatively costly service, when
compared to local calls. It’s rather obvious that in the future cost of international should
decrease significantly and this will also serve as even more powerful consumption externality.
Despite the fact that quoted paper already is two decades old and was written in significantly
different technological environment, it seem that it’s findings are universal and can are
applicable today.
Katz & Shapiro (1986) also provide us with the findings about technology adoption in
industries where network externalities are significant. Author of the master thesis intends to
link some of these findings with relevant examples about the current situation in the 3rd
generation mobile communication industry, where 5 incompatible standards (WCDMA,
CDMA2000, TD-SCDMA, UWC-136, and DECT) for possible technology adoptation are
present:
1. Compatibility tends to be undersupplied by the the market, but excessive
standartization can occur.
This is very the case for a current situation of technology adoptation in 3G phones market.
Despite the fact, that 5 major 3G standarts are present, major mobile phone producers
(Nokia, Motorola, Sony Ericsson) are revealing handsets for only WCDMA standart, as
most network operators which are starting to provide 3G services have chosen WCDMA
platform for the current implementation of 3G. This does not mean, that other standarts
will be forgotten (maybe they are under development or will be developed for the future
3G), but up to date WCDMA has established itself as a leading 3G standart.
16
2. In the abscence of sponsors, the technology superior today has a strategic
advantage and is likely to dominate the market.
According to this finding, WCDMA is superior today even without major sponsoring
activities and will dominate 3G market in the nearest future. This seems rather likely,
because if the WCDMA technology will work as it is intended and will assure qualitative
services according to 3G specifications, there will not be much incentive for network
operators to invest in the creation of the networks based on other competing standarts.
3. When one of two rival technologies is sponsored, that technology has a strategic
advantage and may be adopted even if it‘s inferior.
This might be applicable to WCDMA case – other competing standarts might be have
technical implementation advantages, but as WCDMA was chosen by most important
network components and handsets market players (for the reasons which remain
unknown), it is cleat that this technology has a strategic advantage.
4. When two competing technologies both are sponsored the technology that will be
superior tomorrow has a strategic advantage.
In the 3G market is also applicable, because major network equipment and handset
producers might have chosen WCDMA as a leading standart, because they see that
WCDMA technological capabilities assure it‘s ussage in the future.
Katz and Shapiro (1994) also examine network effect in the presence of systems competition.
First of all let’s look at how authors of the paper define concept of systems: “Many products
have little or now value in isolation, but generate value when combined with others. <…>
Products are strongly complementary, but they need not be consumed in fixed proportions.
We describe them as forming systems, which refers to collections of two or more components
together with an interface that allows the components to work together.” Authors also
separate two types of defined systems:
• Communication networks.
• Systems based on hardware software paradigm.
Communication systems allow various users exchange specific type of messages when they
join the system, which provides the “interface”. Interface is usually created and owned by
service provider and a tool, a component to access the network might be property of user or
17
provider. Obviously in case of telecommunication, that interface is network and components
are the phones. Concept of systems might define fixed telephony as well as mobile
communication industries.
Software - hardware paradigm systems both interface (hardware) and value providing
component (software) are usually purchased by the user (for example PC hardware and
software or CD player and audio CD’s). In some cased “hardware” might be sold under its
production costs and producers get its revenue from the sales of “software” (for example
Microsoft sells Xbox gaming console for ~50% of its production costs).
Typically both types of systems demonstrate observable presence of network effect. In case of
communication network systems “value of the membership to one user is positively affected
when another user joins and enlarges the network”. This is rather usual description which can
traditionally be found in most research papers of Katz and Shapiro. But network effect in
hardware software paradigm network is described differently: consumers form the expectation
about which systems are going to be popular and by buying software they encourage the
producers to achieve economies of scale, which is also specific type of network effect (value
obtained from the network increases as more users join).
Also authors point out three main features of systems competition:
• Expectations
• Coordination
• Compatibility
Rational expectations users form expectations about availability, price and quality of the
components they will be buying in the future. This effect is more obvious in case of software
hardware paradigm, because here users purchase “interfaces” themselves, but effect can be
observed in communication systems as well. In communication systems switching costs also
exist – usually access to the network is not granted for free, for example mobile phone users
must buy SIM card, fixed line phone users must pay phone line installation fee.
System markets also set challenges for the producing firms. Manufacturers taking part in both
type’s systems components and interface production must coordinate their action with other
components and interface producers as well. It is rarely the case when one producer can
successfully produce both interface and components of the system. Also the significant role
there is played not only by the market forces, but also by various industry-wide standard
setting bodies. The impact of standard setting bodies in case of mobile telephony was
addressed in first part of the master thesis.
18
Issue of compatibility between systems is also addressed by the authors, but in rather original
manner. Katz and Shapiro (1994) reject the idea that “incompatibility is just another
coordination failure”, and claim that “obtaining and maintaining compatibility often involves
a sacrifice in terms of product variety or restraints on innovation”. This idea could be used
when analyzing two different cases of introduction of new generation mobile communication
systems, because Katz and Shapiro (1994) pointed out that ”Incompatible systems also can
represent different generations of a single core technology”. So when second generation
mobile communication “interface” was introduced, it’s components (mobile phone) where
not compatible with first generation standard interface, but currently third generation mobile
phones are usually compatible with the existing second generation networks interface. Few
reasons explaining such situation might be pointed out:
• First of all, technically first generation and second generation where completely
incompatible due to very different technology.
• Secondly, third generation services are seen as addition to main second generation
services, usually still within the limited area (highly populated territories) and
available only for those who have technically advanced expensive mobile phones.
This could argument could be also supported support by Katz and Shapiro (1994)
finding: “If the rival systems have distinct features sought by certain consumers, two
or more systems may be able to survive by catering to consumers who care more about
product attributes that network size.”
• Thirdly as network operators have made huge investment in to existing second
generation networks, they upgrade current network component to fit third generation
network, thus experiencing economies of scale, while it is rather costly for the new
operator to enter the market and establish himself as new provider of third generation
network.
So can be said that first and second generation could be called incompatible systems, while
second and third generation systems are compatible. But according to incompatible systems
definition by Katz and Shapiro “two communication networks are incompatible if subscribers
on one network cannot communicate with those on other networks”, we could say that any
generation mobile communication generation users can reach any other generation users by
voice calls; moreover they can communicate with totally different system – fixed telephony
network. Author of the master thesis would suggest update the concept of incompatibility in
following way “two communication networks are incompatible if subscribers on one network
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cannot communicate in any existing network interface using any existing network
component”, because as in this case systems might look incompatible, but actually their
interfaces are be linked.
2.1.2 Stan Liebowitz and Steve Margolis
Two of these modern authors, who question „classical“ network effect paradigms and search
for more applicable models are Stan J. Liebowitz and Stephen E. Margolis.
In their network externalities and network effect definition, included as entry in „The New
Palgraves Dictionary of Economics and the Law“ authors primarily are trying to separate
widely used concepts of „network effect“, „network externalities“ and define them more
precisely:
„The enthusiasm for recognizing and understanding these phenomena should not, however,
lead us to inappropriate or premature conclusions. As we have noted above, there are
distinctions and reservations that ought to be maintained. The first and broadest is that
between network effects and network externalities. A further distinction is between pecuniary
externalities and real ones. Even for the set a real externalities, it is important to note the
distinction between the problem of network size and that of network choice, the boundedness
of the network effect, the likely symmetry of network effects for alternative products, the
ability of large consumers to self-internalize network effects, and differences in tastes.
Liebowitz and Margolis (1995) explain term “network effect” in following manner: “The
circumstance in which the net value of an action (consuming a good, subscribing to telephone
service) is affected by the number of agents taking equivalent actions will be called a network
effect” Author claim, that term “network externality” should be used to describe specific kind
of network effect, where “equilibrium exhibits unexploited gains from trade regarding
network participation”.
People when making a decision about joining a specific network (for example
telecommunication or computer operating system) always inevitably consider how their
participation will affect others and how the participation of others will affect us, meaning that
people consider what the people around them are choosing or are likely to choose.
Liebowitz and Margolis (1995) critically observe that so far the term “network externality” in
the research literature was always associated with positive effect, but that is only one part of
the picture. Negative effects caused by network externalities are also a part of our lifes, for
example when telecommunication or computer network reaches state of overload, any new
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user joining the network will only decrease the utility of other user which they derive from the
consumption. Also there are network, where willingness of other people to join the network
harm each others interests – for example excessive demand for housing in particular area
causes price bubble thus making people pay more than they initially expected. This
understanding of network externalities expands areas of definition usage significantly when
compared to the definition of Katz and Shapiro.
Liebowitz and Margolis (1995) summarize that “goods exhibit network externality wherever
the consumer enjoys benefits or suffers costs from changes in the size of an associated
network, that is, changes in quantities demanded”. Authors notice that benefits and costs
resulting in such situation are directly connected to compatibility, brand familiarity, product
information, status, service availability or the prices of network related goods.
Also Liebowitz and Margolis (1995) add one more interesting dimension to the Katz and
Shapiro classification of networks of communication networks and networks based on
hardware – software paradigm (discussed earlier in the master thesis). Authors also classify
networks according to the ownership of network itself. They notice that in the communication
networks “participants are literally connected to each other in some fashion”, where network
creation requires investment of capital and property rights are always established for such
networks. In such networks users join only with the permission of network owner and use
network according to provided rules. Obviously mobile communication networks belong to
described category. Other type of networks, which correspond to Katz and Shapiro hardware
– software paradigm model, are named by Liebowitz and Margolis as "metaphorical
networks" which are described as providing interrelationships in with no physical connections
used. These networks are not likely to have an owner, because usually it is not possible to
have one. Example could be drivers of particular car brand.
2.1.3 Other authors
Despite the fact that Michael L. Katz and Carl Shapiro works on network effect and network
externalities are widely acclaimed as classical, after some time other authors are trying
critically review all the findings which others authors have made and explain areas of the
theory, which are not completely covered or lack real life evidence. Master thesis author
intends to include few example of such critical approach; one of them is Tim Weitzel, Oliver
Wendt, Falk v. Westarp recent paper “Reconsidering network effect theory”.
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The authors claim that: “While the traditional models greatly contributed to the understanding
of a wide variety of particular (macroeconomic) problems associated with the diffusion of
standards, they fail to explain the phenomenological variety of diffusion courses in today’s
dynamic information and communication technology markets.“
Authors remark that current network externalities theory does not cover the heterogeneous
properties of the markets with new products such as digital television, cellular phones, office
software, Internet browsers, EDI-solutions. These are the markets and products, which where
not present during the time when “classical“network externalities where examined and need
further researches.
In the article, authors try "Systematically reveal deficiencies in the models of positive network
effects by analyzing common assumptions and conclusions, before extending this criticism to
the more general premises of the neo-classical framework.“
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2.2 Diffusion of innovations According to Encyclopædia Britannica2 diffusion of innovations definition:
“Some social changes result from the innovations that are adopted in a society. These can
include technological inventions, new scientific knowledge, new beliefs, or a new fashion in
the sphere of leisure. Diffusion is not automatic but selective; an innovation is adopted only
by people who are motivated to do so <…> Many innovations tend to follow a pattern of
diffusion from higher- to lower-status groups.”
Also mentioned higher status is defined as “young, urban, affluent, and highly educated, with
a high occupational status. Often they are motivated by the wish to distinguish themselves
from the crowd. After diffusion has taken place, however, the innovation is no longer a
symbol of distinction. This motivates the same group to look for something new again.”
2.2.1 Everett M. Rogers Everett M Rogers in his book “Diffusion of Innovations” defines the diffusion of innovations
process saying that innovation is usually communicated “through certain channels over time
among the members of social system”. This means that four key elements are part of the
diffusion process:
• innovation – a product or other objects which is perceived by an individual as new;
• communication channels - intermediary by which messages sent from one individual
or group reach another individual or group;
• time – two time periods are present: innovation-decision process time and
an individual or group innovation adoption process time;
• presence of social system social system.
Moreover Rogers (1963) segments of population which take place in the diffusion process
with their characteristics:
• Innovators - daring and the risk tolerant individuals with substantial financial
resources to absorb possible loss from an unbeneficial innovation. They are intelligent,
have ability to understand complex technical issues and do not feel uncomfortable
with uncertainty of innovation;
2 http://search.eb.com/eb/article?tocId=222921&query=diffusion%20of%20innovations&ct=
23
• Early adopters – usually they are well integrated part of the social system having great
degree of opinion leadership. They are viewed a role models, are respected and
successful;
• Early majority group is described as interacting frequently with their peers, but rarely
hold positions of opinion leadership. They are rather conscious before adopting a new
initiative and constitute to about one-third of the members of a system, thus making
the early majority the largest category taking place in the innovation diffusion process;
• Late majority also counts for about on third of the population. They adopt innovations
by receiving pressure from peers or because of economic necessity. They share
characteristics of being skeptical, and very suspicious.
• Laggards group hold no opinion leadership with point of reference in the past with
limited financial resources.
Actually this segmentation is very applicable to the diffusion process of mobile
communication looking at the markets where mobile communication gradually established
itself going through all technical generations. Innovators where the first users, usually
financially unrestricted and interested in new technology, who purchased first extremely
expensive mobile communication sets and paid very high price for the services. At this stage
mobile communication was very niche market. After that, the wave of early adopters followed
when network coverage became larger. It was mostly institutional and business users. The
only distinction between the early majority and late majority in this case could only be the
difference of financial abilities, because after the technology has advanced, it was only a
question of time when it will became cheaper and will be accessible to most people in most
countries. This segmentation of course is relevant in the countries where mobile
communication was introduced gradually, because countries which adopted already
developed mobile communication technology (for example developing countries adopting
networks of 2G) are in little different situation, when users adopting the technology do not
take any adoption risk. The technology is already tried in other countries and they potential
adopters know technology usability. This means that in such countries only two major groups
could be observed: early majority and late majority, which only differ by financial limitations.
But in this case question arises whether it is really a diffusion of technological innovation.
When product is introduced in the new market not from the first stage of its lifecycle,
different analysis of adoption is required.
24
In addition Rogers (1963) points out five stages of the innovation adaptation process. The five
stages are:
• awareness is described as a stage, where individuals are directly exposed to the
innovation although they lack complete information about it;
• Interest stage starts when an individual becomes interested in the new product or
technology and sees additional information about it.
• Evaluation stage starts when individual psychologically applies the innovation to his
predictable future situation and then makes a decision to try it or not.
• trial stage is considered a period when individual tries to get full use of the acquired
innovation;
• Adoption stage is defined as a moment when the individual makes a positive decision
about continues full use of the innovation.
2.2.2 Bass model
Mahajan and Muller (1979) claim that objective of a diffusion model is to present the level of
reach of an innovation among a potential adopters over time.
Moreover the rationale of the diffusion model is to show the successive increases in the
number of adopters and forecast the continued advance of a diffusion process already in
evolution.
In the product innovation perspective, diffusion models focus forecasting first-purchase sales
of innovations. They also might serve for the development of product life cycle. Mahajan et
al. describe diffusion models as “first-purchase models” assuming that in the product life
cycle timeline there are no repeat buyers (this means that number of buyer is equal to the
quantity of sold product).
The best known and most basic first-purchase model of new product diffusion was defined by
Frank M. Bass (1969).
Main idea of the Bass model lies in assumption that potential adopters of any innovation
receive influence influenced by two means of communication:
• mass media
• word of mouth
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Basically model assumes two groups of potential new technology adopters take place in the
diffusion process. One group receives influence only by external influence channels – mass
media communication. Another group is a subject of internal influence – word of mouth
communication. Bass names the externally influenced group "Innovators" and the internally
group "Imitators". It is important to observe that the role of the groups in the diffusion of
innovation process differs by the timing of the involvement. Innovators are first to receive
information about new product and do have financial means t purchase it and ability to use
independently. This is rather similar to the definition of Rogers (1963). However imitators are
influence by personal connection with the innovators and in this manner the diffusion
happens. Bass model conceptual structure is graphically depicted Figure 3.1. Two curves
depict noncummulative dynamics of technology adoption by making difference between
Innovators and Imitators. Despite the assumption that innovators are usually the early
adopters, however decreasing percentage of the does exist throughout all the time period.
However it is not clear how some individuals can still be influenced only externally when the
curve of internally influenced users reaches its peak. Also one more questionable assumption
of Bass model is observed by Mahajan et al.
Technology adopter distribution assumes that an initial pm (a constant) level of adopters buy
the product at the beginning of the diffusion process. Once initiated, the adoption process is
symmetric with respect to time around the peak time T* up to 2T*. That is, the shape of the
adoption curve from time T*to 2T*is the mirror image of the shape of the adoption curve from
the beginning of the diffusion process up to time T*”. Usually it can be observed that after the
number of adopters reaches peak, market saturation level is not far away. This means that
after realistic curve should have much higher slope after the peak point in the graph.
Simplified Bass model calculating the total number n of technology adopters in time period t
is expressed by the following function:
n(t) = p + q
Where p is a number of innovators (which is calculated by knowing “coefficient of
innovations” – proportion of the potential innovators within population) and q number of
imitators (which is calculated knowing “coefficient of innovators”. Time dimension by having
different time periods for the innovators and imitators is added for more exact calculations.
However it is rather unclear how accurate coefficients of potential innovators and potential
26
imitators can be objectively obtained in the real product market. Bass model is extensively
quoted, interpreted and expanded in the innovations literature during last three decades. Few
interesting extensions of the model are provided below.
Tanny and Derzko (1988) imply that concepts of „Innovators" and "Imitators" used in Bass
model do not precisely describe characteristics of buyers taking part in the technology
adaptation process.
They offer an addition of the model in which all potential adopters are divided in two
distinctive groups which they label “Potential Innovators” and “Potential Imitators”. Potential
Innovators as well as Potential Imitators are a subject of the mass-media communication
influence, but only Potential Imitators are influenced by word of mouth, where Potential
Innovators are free of this influence. This seems really reasonable, because Potential Imitators
also receive also receive external mass media influence, but it might be, that this influence has
different consequence on their decision making process. Also as it was pointed out by Rogers
(1962) it might be that different groups might receive different quality of external media
provided information (for example people with more financial resources do have access better
quality information channels – satellite TV, international press).
One more interesting observation was made by Mahajan et al. (1990). A key characteristic of
the Bass model is that it addresses the market in the aggregate manner, measuring the number
of two groups adopters who acquire the product in time period t. Mahajan et al. (1990) raises
subsequent question: “Can the diffusion model be built by aggregating demand from
consumers who behave in a neoclassical microeconomic way? That is, assume that potential
adopters are smart and are not just carriers of information. They therefore maximize some
objective function such as expected utility or benefit from the product, taking into account the
uncertainty associated with their understanding of its attributes, its price, pressure from other
adopters to adopt it, and their own budget.”
This means that every single adopter both from imitators and innovators groups do have more
characteristics that it is taken in to account in the Bass model. That it is why one more
dimension should exist – it is probability of adopting the product in time t for consumer with
its specific characteristics. Mahajan et al. mentions several parameters which could be added
to the Bass model: individual uncertain perception of the innovation's performance, predicted
future value and benefits from the innovation. So only by looking at the micro level of
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potential adopter, we can assume that individual will adopt the innovation when “his utility
for the innovation becomes greater than the status quo (he is better off with the innovation)”.
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2. External – regulation factors influencing growth In this section three articles three relevant articles are analyzed. John Kruse article discusses
the methods of conducting spectrum allocation. Harald Gruber article analyzes the role of the
regulation in the process of growth. This article provides us with the alternative example of
spectrum allocation in Hong Kong.
3.1 Methods of allocation of scarce resources The study of Jorn Kruse (2002) examines various methods and options available for the
allocating scarce spectrum resources. Brief summary of the discussed methods is provided
below.
Kruse (2002) already in the introduction mentions that current developments of the mobile
telephony market can be characterized by low level of regulation in most countries, only with
significant exception of spectrum licensing.
Also he ads, that spectrum allocation methods by large shape industries and market structures
in the individual countries and as spectrum is a scarce resource, it is very important that
allocation lead to desired results of the efficiency of competition in the market.
Author remarks that “Spectrum is not only a technically essential resource. The availability of
more or less spectrum (and what kind of spectrum) is of primary importance for the economic
success of a mobile operator. Adequate spectrum allocation is a crucial factor, if a mobile
sector will be competitive and efficient or not.”
Kruse (2002) emphasizes the role of government (spectrum regulating body) in the process,
because its action has a strong impact on important characteristics of the market: the number
of the markets players, standards, competition rules etc.
Also author ads that spectrum can be described as an essential resource and the allocation of
spectrum is really significant for the level of competitiveness of operator and has an impact on
the competition and the efficiency of the whole industry.
Kruse (2002), before going into details of various spectrum allocation methods, explains
important the concepts of Intramodal and Intermodal Spectrum Allocation.
29
Term Intramodal Spectrum Allocation is used to describe the rivalry between similar services
operating within the same frequency thus creating the situation when spectrum becomes
subject to consumption rivalry.
Intermodal spectrum allocation describes the allocation of spectrum to different services (TV,
cellular mobile communication, or emergency). The allocation of the intramodal spectrum
dimension is traditionally decided by regulating body. However in the literature ideas
promoting the free market standardization process can be found.
Following methods for Frequency Resources Allocation are used by various countries
regulating bodies:
1. First-come-first-served was most commonly used during early days of
telecommunication, when monopoly situation existed. Companies (usually state
controlled) where granted with the licenses without any competition. It was just a
bureaucratic procedure without any intervention of the market forces. Nevertheless
this allocation method was mostly used for the first generation mobile communication,
but also in some countries first generation license holder was granted with the second
generation license. However this method is not practiced in the market economies
nowadays.
2. Lotteries method also is rather rare case, but I was used in United States local digital
markets, because the procedure is very simple and rarely a subject for legal
complaints. However method does not warrant efficient use of spectrum, because
second hand trade even prohibited by the regulators might take place using license
holding company acquisition.
3. Auctions are most commonly used method nowadays for spectrum allocation, because
they have characteristics of being non-discriminatory and transparent. Also depending
on the auction design they have relatively low transaction costs. As the market forces
operate in this method, it is most likely that it will lead to the efficient use of
spectrum. Also auctions are not very vulnerable for the potential corruption because
influencing bodies do not leave much of discretionary control. Sometimes (as with the
most European 3G licenses) they are seen as a source of significant contributions to
the countries’ budgets. Auction designs can have few forms according to their
institutional settings and rules:
30
• The English Auction is most standard auction procedure also commonly used in all
other market niches where auctions are conducted. It starts from a low price, when a
bidder overbids the prevailing bid in each round. Auction ends when no higher price is
offered and the remaining highest bidder is getting the license at his offered bid price.
However the relatively simple procedure might become complicated when more than
one license is offered simultaneously (frequencies in different regions).
• The Dutch auction is an opposite way when auction starts from a high price, which is
automatically lowered if no bidders stop descending procedure.
• In the First-price sealed-bid-auction mechanism the bidders to submit a single (non-
renewable) bid. The highest price bidder gets the spectrum license at his offered price
• In the Second-price sealed-bid-auction or Vickrey auction, the winner pays the price
of the second highest bidder.
However most auction researchers agree that even simple auctions mechanisms are subject to
the risk of collusion, depending on the specific auction method. Even in the English auctions,
where it looks like incentives for cheating are almost non-existing, comparatively high risk of
collusion exists. By saying that Kruse (2002) remarks that most of European spectrum
auctions have been English auctions.
4. Discretionary decision making is method which is fiercely criticized by free market
economists’ because it usually is associated with corruption and fraud, as the
regulating bodies have highest authority to choose the winner according to heir own
standard. It might happen that these standards are set favorably for the companies
which support ruling parties financially or even to family owned companies. Despite
the fact this that most discriminating and intransparent it is still practiced in some
African countries.
5. “Beauty Contest” is procedure when potential candidates are evaluated on clear and
transparent criteria by government regulating bodies or independent commissions.
Sometimes this method includes auction elements or might serve for the creation of
potential candidates list (Honk Kong spectrum licensing case will be discussed later in
the thesis).
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Kruse (2002) concludes the discussion about spectrum allocation mechanisms by stating that
“In general, economists strongly advocate auctions. But in practical terms, this very much
depends on the specific institutional framework and action design.”
Kruse (2002) also criticize European 3G license auction by saying that in spectrum auction,
unlike in other traditional market transaction where auction are employed, the objective
should not be the maximization of revenues, but “The proper functioning and efficient
outcome of the mobile markets are supposed to be the main objectives for the spectrum and
licensing auctions”. European 3G licensing will be discussed in more details within this
thesis.
Author also suggests questions which should be taken into account when developing a
spectrum allocation mechanism. They should include:
• Length of license duration and renewability.
• Spectrum tradability on a second hand market.
• Time schedule of auctions in relation to technological and market developments.
• Step-by-step-licensing or simultaneous licensing in the market.
• Advance notification of participation in the auction.
3.2 Role of regulation Harald Gruber (2000) in his articles tries to answer the question what impact does the scarcity
of frequency spectrum makes on the performance of the mobile telecommunications industry.
Also in the paper author provides insights about the role of spectrum allocation together with
most important developments during the existence of spectrum allocation period. Brief
summary of the most relevant points of the article is provided below.
Author starts the article by emphasizing the importance of spectrum allocation in nowadays
mobile communication industry. The competition for the scarce frequency resources started
when first non monopoly operators where allowed to act as service providers. Despite the fact
that mobile telecommunications industry is constantly improving the spectrum efficiency used
for the services, the increasing popularity of mobile telecommunications services puts on
additional pressure for allocating even more frequency spectrum. Also as mobile
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telecommunication is highly profitable business, with financially powerful operators,
spectrum licenses are becoming continue sly expensive.
Gruber (2000) reminds that from the of telecommunication industry, mobile communication
was seen as a natural monopoly, for the rational reason, that due to technically not advanced
systems, frequency spectrum availability was very scarce with poor efficiency of resource
usage. But regulators and other public bodies became more and more concerned that the
absence of competition in the industry leads to inadequate incentives to decrease costs and
also provides no suitable environment innovation with observable under-investment.
Frequency competition was born after the introduction of more advanced second generation
mobile communication systems, when some countries started issuing more than one license
for the providers of mobile communication services. After that the idea of fees for issuing the
licenses came into place. Thus mobile communications industry became the first major space
of competitive supply of telecommunications services.
Gruber (2000) claims that government licensing policy in mobile telecommunications has
following dimensions:
1. Government needs to make decision about a single national standard or leave this
decision for the market where multiple technological systems can compete.
2. Government need to define the optimal number licenses to grant as well as timing on
issuing
3. Government needs to develop suitable licenses allocation method.
In the beginning GSM licenses were allocated mainly using “beauty contests” allocation
method in Europe. Also in some countries incumbent fixed line telecommunications operators
where granted with the licenses. However it was obvious that it can be only a temporary
solution, because as the market labialization was increasing (mainly due to impact of the
intereueopean regulation) competitive interaction among bidders was constantly increasing.
Gruber (2000) provides reflection of these trends using statistics about various countries
telecommunication sector: from 118 countries which adopted first generation mobile
communication systems 75% had monopoly, while from the 87 countries which adopted
second generation mobile communication systems 56% already had oligopoly.
Interpreting that Gruber (2000) makes an implication that “Capacity thus seems to be a first
crucial factor in explaining the effects of competition on the diffusion of mobile penetration.
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When capacity is constrained, as under the analogue technology (especially during the early
years), the effects of competition on mobile penetration are likely to be modest. The effects of
competition are potentially much larger under the digital technology when capacity
constraints are relaxed.”
Gruber concludes the article by saying that the scarcity of frequency spectrum is still the
major concern of the mobile telecommunications industry. Despite the significant
technological progress that industry made during last decade, scarcity of the spectrum still has
limitation for the providers of the services.
3.3 Alternative approach
This research paper of Xu Yan (2004) is reviewed in this master thesis as unique and
successful example of alternative approach of spectrum allocation. Hong Kong‘s spectrum
regulating body Office of Telecommunications Authority after intensive debate and
consultation formulated 3G licensing scheme, which is very different from the traditional
spectrum licensing auctions used in Europe. Brief review of these differences and steps which
where taken upon reaching the unique solution is provided flowingly.
In the beginning of the research paper Xu Yan (2004) discusses difficulties, risks and
constraints which are inevitable when conducting spectrum auction in traditional manner.
These will not be reviewed there as it was discussed earlier in the thesis.
Also Xu Yan (2004) ads an interesting perspective of the problem by stating that the
difficulties of formulating a harmonizing licensing framework primarily are caused by
different economic backgrounds and existing spectrum allocation situation which determine
aspects of future spectrum allocation.
Analyzing the mentioned economic backgrounds and existing spectrum allocation situation
Xu Yan (2004) remarks that Hong Kong government (unlike some governments in EU) has
no need to raise significant revenue through spectrum auctions and can trade spectrum at
relatively liberal pricing scheme. This gives two benefits:
1. Licenses can be obtained not only by the most financially powerful operators.
2. Companies which get the licenses are not financial exhausted and can invest in to
R&D for more innovative new services or have services provided at lower prices.
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Xu Yan (2004) also insists that that 3G as a high-tech value adding industry with social
benefit much higher than the commercial benefit and will produce significant externalities if
any unfavorable situation developed. That‘s why government should take actions to reduce
the risk of potential investors. The most appropriate for the government for reducing the risk
is reducing operator’s financial risks by using convenient license pricing.
Knowing that Office of Telecommunications Authority eventually rejected primary proposal
of auctions and started the discussion about how the spectrum allocation process might be
constructed. At first it was only agreed that the practice that traditional procedure similar to
‘beauty contest’ will be used for formulating the potential license candidates list.
Also Xu Yan (2004) mentions that the allocation of radio spectrum was a non complicated
issue in Hong Kong, because most of the 3G defined spectrum has not been occupied by other
services and four 3G licenses can be issued.
However four 3G licenses is relatively small number taking into account that where issued 11
licenses in Hong Kong 2G market. The idea of the proposed solution how to deal with this
problem came originally from Europe – model very similar to Mobile Virtual Network
Operator concept (which exists in EURO) was offered. Mobile Virtual Network Operator is
based on the idea that some operators which do not have assigned radio spectrum can use the
proportion of spectrum of original Mobile Network Operators and be allowed to build and
operate parts of the networks which do not require the use of radio spectrum. When this
solution is used Mobile Virtual Network Operator are able to offer 3G services using their
own brands but not operating the radio networks, thus reinforcing the competitive market
model even more.
It was proposed that successful bidders which will be granted with the licenses will have the
obligation to open at least 30% of their 3G network capacity for usage of non-affiliated
Mobile Virtual Network Operator companies and/or content providers.
Concerning the pricing of licenses it was decided that price of the license should be negotiated
commercially. Only If commercial negotiation are not successful, the regulator has the right to
make own determination based on principles of fair interconnection, thus finding a balance
between to low price (‘‘free-rider’’ phenomenon) and a sufficient investment return on cost
of capital ( thus reflecting the higher risk of 3G service investment) . Mobile Virtual Network
35
Operator and content providers will buy negotiate the tariffs with Mobile Network Operator.
Negotiated tariffs might be a subject of regulators intervention and should reflect mentioned
return on the cost of capital.
Also the pre-qualification process was presented which was intended to be relatively liberal,
involve setting minimum criteria on investment, network rollout, service quality, financial
capability. Moreover very original license pricing scheme was introduced. Bidders where
asked to bid for a level of annual royalty (as a percentage of turnover) from 3G services
network operations. However for the first 5 years of operations, minimum royalty payment
will fixed by the government, because it will be complicated to distinguish between second-
generation mobile service (2G) and 3G network revenues, as most of 3G licensees will be an
existing 2G operators.
Starting from year six to the end of the license period, 3G Mobile Network Operator will pay
royalties to the Government according to the royalty percentage which is determined by the
auction. The calculate actual royalty payment of course will different for different Mobile
Network Operator as they will have different 3G revenue turnover.
Concluding the research paper Xu Yan (2004) states that „It is important to point out that the
open network obligation of 3G licensees has been fully debated before the licenses were
auctioned, and all bidders have been very well informed about the potential competitiveness
of the 3G market. As a result, no bidders should be so nave as to unreasonably overbid the
licenses, especially in the context that several European licensees are suffering from the
winner’s curse. “
In this way government also is not losing either, because the continues royalty payments will
allow the government strengthen possibly unstable future 3G services market with almost no
financial risk. Hong Kong’s 3G licensing scheme might be seen a balance between scarce
spectrum efficiency and working market competition thus providing an alternative way of
thinking about the problem.
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4. Empirical analysis 4.1 Example of empirical research
4.1.1 Massini’s research
Massini’s (2004) study investigates the diffusion of mobile telephony in Italy and UK using
the 1990s. Massini used the following data variables for its research:
• Subscribers Monthly series
• Tariff Average annual revenue per subscriber
• Price of the Handset
• Final Consumption Expenditures,
• Consumption Price index to deflate the series of the price of the
• Active population
Author defines an aggregated diffusion model, based on the standard epidemic model, by
introducing economic variables.
In addition to the economic variables authors also ads variable that takes into account the
effect of the technological change which occurred in mobile telephony during the researched
period. This change was the shift from analogue (1G) technology to digital (2G) technology.
This variable also might be interpreted as a factor for quality changes.
In the paper author defines and estimates diffusion curves of mobile telephony users in Italy
and the UK during the 1990s, using standard epidemic model and taking into account of
economic and technological factors that are helpful in explaining differences of the diffusion
speed. Also they suggest methods to differentiate long-run relationships and short-run
adjustments of continuous diffusion.
Interesting remark is that, two countries have very similar characteristics (size, wealth,
population and geographical features), exactly the same mobile telephony services
introduction year (1985). Even so investigation market structures and institutional regulation
frameworks, shows remarkable differences in two countries: Italy had near monopoly
situation until the introduction of the second operator in 1995, while in the UK the service
where started from duopoly model which was enriched by the entry of a two more operators
in 1994.
Also author notices, that the strategies local mobile network operators employ, shows
observable differences in Italy and United Kingdom. Italian operators where primarily
competing by introducing innovative pricing packages, including the introduction first
37
prepaid SIM card in 1996. British operators chose to use subsidies for the cost of the handset
thus locking customers to their networks.
This resulted in differing portfolios of clients in both countries with different tariff packages
in both countries:
• Large number of prepaid cards and pay as you go contracts, which do not require high
connection fees.
• Large number of subscribers to knowing their own needs and operator’s
characteristics with discouragement to switch network.
Massini (2004) remarks that “Although the prepaid customers tend to spend less than contract
customers and reduce the average revenue per subscribers, the success of prepaid services in
Italy and UK has been such that the total profits for the operators are growing continuously.”
Also author insists that improved marketing of services and information presentation was
employed by the operators because of constantly increasing variety of products and services
which ads substantial complexity and confusion when buying a mobile phone.
Before conducting the empirical investigation, author extensively explains the diffusion
process of S-shaped profile by describing to counteracting forces involved in the process.
First force is the increasing number of the adopters which acts positively on the diffusion; the
second force is decreasing number of potential adopters.
Author also notices that “the epidemic feature is not simply spread of information about the
existence of the innovation, but also spread of information concerning its technological
characteristics and the increase in post-adoption profitability”. This can be seen as a wise
strategy for the developers and promoters of any innovation: it is necessary to reduce the
potential adopters decision making process by reducing degree of uncertainty associated with
the innovation. As more information about the innovation is available from various sources,
critical mass of the adopters is reached.
Massini (2004) finds out those profiles of the number subscribers of mobile telephones shows
typical S-shaped curve both for Italy and the UK. Also author observes that number
subscribers of analogue first generation technology reached its maximum saturation level both
in Italy and the UK in 1996, after which the decline follows.
As it is typical for competing product generations the two technologies had showed same
market shares in 1997, but in both countries analogue telephones started to disappear while
digital telephones have grown even faster after that.
38
Also author remarks that from the point of view of the operators, the cost of the infrastructure
for the digital technology due to some specific technical reasons was lower and required much
smaller investment when compared to the analogical technology. This means that service
providing even better quality can be cheaper that lower quality old generation service,
meaning that there is no real competition between technologies and switching of consumers is
only a question of time
Author adds that:”The profiles of the subscribers of the analogue and digital technologies
follow the characteristic pattern of technological substitution between successive generations
of technologies, where a new generation cannibalize the previous one and will be
cannibalized by the next generation”
Author summarizes the following results of the most important in the researcher:
1. The assumption that the introduction of the digital technology (2G) had a significant
improvement for the Italian subscribers, but not significant for UK subscribers is
strongly supported by empirical results. Author gives a following explanation to this
finding: “If the trend represents learning factors it could be argued that the British
market was already quite mature when the digital technology was introduced and the
learning processes on the new technology have been increasingly less important. “
2. Result show is that economic variables have an important in both the diffusion speed
and the saturation level. Most important variables where tariffs and price of handset.
Price of the handset was less significant in UK that in Italy and this is explained by the
fact that handsets are traditionally largely subsidized in UK. Tariffs where highly
important in both countries
3. Author applies the Error Correction Model and gets the result which demonstrates
“that the variables affecting the diffusion speed tend to be significant in the long run,
but they do not affect the process in the short run, and they do not explain the
deviations from the long run pattern.”
Author suggests for future researchers to add more factors:
• market structure;
• infrastructure indivisibility;
• some characteristics of the handset,
39
• the coverage rate;
• information on the fixed lines, like its relative costs compared to the mobile network;
• longer and updated data on the three generations of mobile communication
technology.
4.2.1 Botelho research
The recent paper of Botelho, Pinto (2004) paper analyses the pattern and rate of adoption of
mobile telephones in Portugal. Time-series data on the number of subscribers is analyzed
using statistically based method to estimate the market potential for mobile phones is
employed, allowing generation of confidence intervals about the estimated market potential.
The nature of the used method allows researchers not only to empirically derive the expected
market potential, but also to determine the current stage of the market with respect to
saturation level and make observations about growth process.
Botelho et al. (2004) estimated the S-shaped diffusion functions (exponential, Gompertz, and
logistic) growth models using time series data on the number of cellular phone subscribers) by
ordinary least squares method for exponential function and nonlinear least squares for
Gompertz function. The find out that:
1. Exponential growth model has high coefficient of determination and high t-statistics,
but model clearly overestimates number of cellular phone subscriber’s growth rate.
Author concludes that exponential growth curve does not provide a very realistic
description of the cellular phone diffusion in Portugal.
2. Results using the Gompertz model show that the saturation of Portugal mobile phone
users market is 25% than Portugal’s population rate and author declares this as
unreasonable suggesting that Gompertz model does not provide accurate forecasts of
the growth phenomenon.
However article was originally written in 2001 and the last data used is from 2000. Recent
mobile phone user’s statistics show that in some countries the saturation level has already
overcome 100%. This is possible because increasing proportion of population is using few
SIM cards for various purposes.
3. High t-statistics for each of the estimated parameters in the logistic model results
indicates an excellent fit with the data. Also logistics model demonstrates a realistic
saturation level of 67% of population.
40
Author concludes the paper saying that conducted research demonstrates that the adoption of
cellular phones in Portugal can be explained by S shaped -curve.
Also he ads that “although both the Gompertz and logistic models describe a sigmoid
diffusion curve, it is only the logistic model that adequately describes the path of cellular
phone diffusion in Portugal.”
The reason for this is rather technical: the Gompertz model is derived from a skewed
frequency distribution, while the logistic model is based upon a symmetric frequency
distribution which fits researched situation much better. Also logistics model depicts the rate
of diffusion in Portuguese cellular phone market better because it is closer to symmetric,
while reaching its maximum growth at an earlier phase as Gompertz function suggests.
Functions
Botelho et al. (2004) provides us with the specification of the traditionally used functions
which depict S-shaped diffusion process. As author of the master thesis also performs
statistical data analysis similar to the one conducted by Botelho et al., overview of these
functions with brief descriptions is provided flowingly.
Botelho et al. (2004) explains the usage of S-shaped diffusion model in following manner:
“Irrespective of the particular account of the diffusion process, the stylized diffusion path of
most innovations results from the fact that initially, during an embryonic phase, only a few
members of the social system adopt the innovation. Over time, though, an increasing flow of
new adopters is observed as the diffusion process unfolds. This is the phase of rapid market
growth. Finally, during a maturing phase, the trajectory of the diffusion curve gradually slows
down, and eventually reaches an upper asymptote or saturation level.”
Traditional evolutionary pattern S-curve is expressed a differential equation:
41
Where:
• y(t) is the cumulative number of subscribers at time point t;
• y* is the saturation level
• γ is the coefficient of diffusion.
Main assumption of the model is that number of subscribers growth rate is positively
influenced by the number of existing subscribers and the difference between the saturation
level and the number of existing subscribers.
Mostly used functions of S-curves representing are the logistic, the Gompertz and exponential
functions. Each of them is described below.
The Gompertz function is expressed as
_________________________________________________________________________
Where:
• yt is the number of existing subscribers at time t;
• k; a; and b are parameters to be estimated.
The Gompertz function values ranges from a lower asymptote of zero to the upper bound k,
while t ranges from negative infinity to positive infinity. The parameters a and b are used for
determining the location and the shape of the curve.
The logistic function is expressed as:
Where:
• k determines the upper bound of yt;
• parameters a and b determine the location and shape of the curve.
The logistic curve reaches its maximum growth rate kβ=4 when yt ¼ k=2. The logistic curve
is symmetric about its inflection point.
42
The exponential function is expressed as: :______________________________________
Where:
• ln yt is the natural logarithm of the number of existing subscribers at time t;
• coefficient b determines the constant proportional change in the variable y.
4.2 Empirical data analysis In this section empirical data analysis using various statistical techniques is conducted.
First of all correlation between number of Lithuanian mobile phone subscribers and economic
variable will be analyzed (GDP, average income). This should give an answer to the question
weather decision to become a mobile phone user is influenced by economical factors. Also
correlation number of Lithuanian mobile phone subscribers and the number of fixed line
subscribers will be employed, to answer the question weather the decision to become a mobile
phone user is influenced by technological substitution factor.
Secondly regression is employed also to test the relationship between number of Lithuanian
mobile phone subscribers and mentioned independent variables (GDP, average income,
number of fixed line subscribers). Additionally to the simple regression, multiple regression
models are used to test to test the relationship between variously paired independent variables.
Thirdly number of Lithuanian mobile phone subscribers is analyzed using time series method.
Different functions for time series are employed and analysis is should provide an answer to
the question which of which of the used function can describe the variation precisely. Also
exponential function is analyzed as in the Botelho, Pinto (2004) research and results are
compared.
Data on the number of Lithuanian mobile phone subscribers for the research is available for
the period 1996 – 2004, as 1996 was the first year when data about generation mobile
communication was started to be collected in Lithuania.
43
Table 4.1 Cumulative data of mobile phone and fixed line subscribers.
Time
period
Mobile
subscribers,
thou.
Fixed line
subscribers,
thou.
1996 51 992,6
1997 150,8 1048,2
1998 267,6 1109,8
1999 343,6 1144,6
2000 508,9 1180,1
2001 1018 1144,5
2002 1631,6 929,6
2003 2152,6 827,8
2004 3421,54 776
Source: Lithuanian Statistical Department, (http://www.std.lt)
GDP data and average income is adjusted is adjusted according to 2000 price level.
Table 4.2. Economical variables
Time
period
GDP, in
thou LTL
Average
income,
LTL
1996 38821 618,2
1997 41541 778,1
1998 44565 929,8
1999 43810 987,4
2000 45526 970,8
2001 48429 982,3
2002 51704 1013,9
2003 56716 1072,6
2004 60511 1157,8
Source: Lithuanian Statistical Department, (http://www.std.lt)
44
4.2.1 Correlation analysis
In Table 4.3 the results of strength of relationship between number of mobile phone
subscribers and yearly GDP is demonstrated. We can see that Pearson correlation coefficient
implies that positive correlation with very strong relationship exist between variable.
Coefficient value 0,974 is very near to perfect correlations, meaning that the variables are
precisely related. Also we can observe that this correlation is statistically significant, with
probability value lower than 0,001.
Table 4.3 Correlation. Number of mobile phone subscribers and GDP.
Correlations
Mobile_subscri
bers_thous GDP
Pearson Correlation 1 0,974
Sig. (2-tailed) 0,000
Mobile_subscribers_thous
N 9 9
Pearson Correlation 0,974 1
Sig. (2-tailed) 0,000
GDP
N 9 9
**. Correlation is significant at the 0.01 level (2-tailed).
Scatter diagram of variable (Figure 4.1) also demonstrates that when one variable increases,
another variable behaves in very predictable way:
45
Figure 4.1. Scatter diagram for mobile phone subscribers and GDP
In Table 4.4 we can see the results of relationship between number of mobile phone
subscribers and average income. Pearson correlation coefficient value 0,773 demonstrates
rather strong positive relations between variable. However we can clearly observe that
relationship between the variables is statistically insignificant with probability value 0,015.
Table 4.4 Correlation. Number of mobile phone subscribers and average income.
Correlations
Mobile_subscri
bers_thous
Average_Incom
e
Pearson Correlation 1 0,773
Sig. (2-tailed) 0,015
Mobile_subscribers_thous
N 9 9
Pearson Correlation 0,773 1
Sig. (2-tailed) 0,015
Average_Income
N 9 9
*. Correlation is significant at the 0.05 level (2-tailed).
46
Also we can observe from the scatter diagram (Figure 4.2) there is no clearly observable
pattern of relationship between variables.
Figure 4.2. Scatter diagram for mobile phone subscribers and average income.
Table 4.5 represents the results of strength of relationship between number of mobile phone
subscribers and number of fixed phone line subscribers. We can see that Pearson correlation
coefficient implies that negative relationship exist between variables. Coefficient value -0,818
is reasonably high, demonstrating those variables are strongly negatively precisely related.
Also we can observe that this correlation is on the boundary of statistical significance, with
probability value 0,07.
Table 4.5 Correlation. Number of mobile phone subscribers and fixed subscribers
Correlations
Mobile_subscri
bers_thous
Fixed_subscribe
rs_thous
Pearson Correlation 1 -0,818
Sig. (2-tailed) 0,007
Mobile_subscribers_thous
N 9 9
Fixed_subscribers_thous Pearson Correlation -0,818 1
47
Sig. (2-tailed) 0,007
N 9 9
**. Correlation is significant at the 0.01 level (2-tailed).
Also we can observe from the scatter diagram (Figure 4.3) that relationship between variables
exist, but it is not expressed very explicitly.
Figure 4.3. Scatter diagram for mobile phone subscribers and fixed line subscribers
4.2.2 Regression analysis
In the following simple and multiple regression models will be used to study the nature of
relationship between number of mobile phone users and variables used in the correlation
analysis.
Simple regression will be conducted between numbers of mobile phone users as dependent
variable and GDP, average income, number of fixed line subscribers. Also in the multiple
regression independent variables will be paired in following manner: GDP and fixed line
subscribers; average income and fixed line subscribers
48
Conducting simple regression for number of mobile phone subscribers and number of fixed
line subscribers we get results depicted in Table 4.4. Following regression equation can be
constructed:
Mobile phone subscribers = 7.548 – 6,379 x Fixed line subscribers
Interpretation of the obtained equation could be following: every unsubscribed fixed phone
line resulted in 6,379 mobile phone subscription in period 1995-2004. This seems rather
obvious – usually household in Lithuanians has one fixed line, but after unsubscription of this
line every household member purchases personal mobile phone.
Table 4.6 Regression coefficients. Mobile phone subscribers and fixed line subscribers
Unstandardized
Coefficients
Standardized
Coefficients Model
B Std. Error Beta
t Sig.
(Constant) 7.548,067 1.740,016 4,338 0,003
1 Fixed_subscribers_thous -6,379 1,695 -0,818
-
3,762 0,007
But we can see from the Table 4.6 that in the obtained regression equation variation of
independent variable explains only 66.9% of variation of the dependent variable. It can be
said that this equation can not be seen as good predictor because about one third of variation
remains unexplained. However Durbin-Watson very coefficient demonstrates that equation
can not be used for prediction.
Table 4.7. Regression characteristics. Mobile phone subscribers and fixed line
subscribers
R R
Square
Adjusted
R
Square
Std. Error of
the Estimate
Durbin-
Watson
0,818 0,669 0,622 700,75193 0,657
49
Conducting simple regression for number of mobile phone subscribers as dependent variable
and number of fixed line subscribers as independent variable we get results depicted in Table
4.7. Following regression equation can be constructed:
Mobile phone subscribers = -6.393 + 0,155x GDP
The constant coefficient B might be interpreted flowingly: as the GDP reaches 41,000 thou.
LT (-6.393 divided by 0,155) first mobile phone subscribers can be registered. However this
measure can not be used as an objective predicting coefficient, because different countries do
have different GDP level and this does not clearly explain cause and effect.
Table 4.8 Regression coefficients. Mobile phone subscribers and GDP
Unstandardized
Coefficients
Standardized
Coefficients Model
B Std.
Error Beta
t Sig.
(Constant) -6.393,482 663,6
90 -9,633 0,000
1
GDP 0,155 0,014 0,974 11,341 0,000
We can see in the Table 4.9 that in this regression model 94.8% of dependent variable is
explained by variation of dependent variable. This means that it is very precise model and can
be used for prediction. Durbin Watson coefficient is not significantly far from 2.
Table 4.9. Regression characteristics. Mobile phone subscribers and GDP.
R R
Square
Adjusted
R
Square
Std. Error of
the Estimate
Durbin-
Watson
0,974 0,948 0,941 276,76174 1,527
Following regression equation is designed to test the relation between mobile phone
subscribers as dependent variable and average income as independent:
50
Mobile phone subscribers = 4.144+5,505x Average Income
Interpreting the equation we can say that every average income increases by 100 Litas ads 550
thousand new mobile pone subscribers.
Table 4.10 Regression coefficients. Mobile phone subscribers and average income
Unstandardized
Coefficients
Standardized
Coefficients Model
B Std. Error Beta
t Sig.
(Constant) -
4.144,7891.633,362
-
2,538 0,039
1
Average_Income 5,505 1,706 0,773 3,227 0,015
However this regression equation can be evaluated as rather misleading, because we can see
from Table 4.11 that only 59,8% of independent variable variation is explained by dependent
variable variation. Also Durbin-Watson coefficient is far below the needed level of 2.
Table 4.11 Regression characteristics. Mobile phone subscribers and average income.
R R
Square
Adjusted
R
Square
Std. Error of
the Estimate
Durbin-
Watson
0,773 0,598 0,541 772,35124 0,556
Following regression equation is derived from the Mobile phone subscribers as dependent
variable and pair of fixed line subscribers and GDP as independent:
Mobile phone subscribers = -3.448 - 1,69 x Fixed line subscribers + 0,13 x GDP
We can interpret that when 1 fixed line subscriber decides to discontinue the usage of this
service, 1,69 additional mobile phone subscribers is observed. Also we can add that as GDP
increases by 1000 LT, additional 0.13 subscribers join the mobile communication network.
51
Table 4.12. Multiple regression coefficients. Mobile phone subscribers, fixed line
subscribers and GDP.
Unstandardized
Coefficients
Standardized
Coefficients Model
B Std.
Error Beta
t Sig.
(Constant) -3.448,548 1.534,1
56
-
2,248 0,066
Fixed_subscribers_thous -1,690 0,822 -0,217 -
2,056 0,086
1
GDP 0,130 0,017 0,814 7,717 0,000
R square coefficient and Durbin Watson statistics in Table 4.13 demonstrate perfect fit;
however significance level is marginally higher than desired.
Table 4.13 Multiple regression characteristics. Mobile phone subscribers, fixed line
subscribers and GDP.
R R
Square
Adjusted
R
Square
Std. Error of
the Estimate
Durbin-
Watson
0,985 0,970 0,960 228,98033 2,258
Following regression equation (from Table 4.14) is derived from the mobile phone
subscribers as dependent variable and pair of fixed line subscribers and Average Income as
independent:
Mobile phone subscribers = 2.271 - 4,861 x Fixed line subscribers + 3,947 x Average Income
In this equation we can say that when as every fixed line subscriber discontinues the usage of
the service, 4.86 additional mobile phone subscribers join the network, and every 1 LT of the
additional average income brings 3.947 additional subscribers of mobile communication
networks. This is clearly overestimation despite the fact that R square and Durbin Watson
52
statistics demonstrate near perfect characteristics. Also constant coefficient significance is far
above the needed level. (Table 4.15)
Table 4.14. Multiple regression coefficients. Mobile phone subscribers, fixed line
subscribers and average income.
Unstandardized
Coefficients
Standardized
Coefficients Model
B Std. Error Beta
t Sig.
(Constant) 2.271,734 1.307,927 1,737 0,133
Fixed_subscribers_thous -4,861 0,842 -0,623 -
5,772 0,0011
Average_Income 3,947 0,769 0,554 5,135 0,002
Table 4.15 Multiple regression characteristics. Mobile phone subscribers, fixed line
subscribers and average income.
R R
Square
Adjusted
R
Square
Std. Error of
the Estimate
Durbin-
Watson
0,969 0,939 0,918 325,88589 1,826
4.3.3 Curve estimation
In the following section cumulative data of cellular phone subscribers (Table 4.1) is analyzed
using time series curve estimation.
First of all data is analyzed using various times series functions: Linear, Logarithmic, Inverse,
Quadratic, Cubic, Compound, S-curve, Growth, Exponential.
Table 4.16 summarizes the obtained results:
53
Table 4.16 Time series functions results.
Equation Model Summary
R
Square F df1 df2 Sig.
Linear ,841 36,909 1 7 ,001
Logarithmic ,607 10,815 1 7 ,013
Inverse ,353 3,824 1 7 ,091
Quadratic ,987
220,09
8 2 6 ,000
Cubic ,995
338,16
5 3 5 ,000
Compound ,977
303,01
9 1 7 ,000
Power ,958
160,37
4 1 7 ,000
S ,785 25,527 1 7 ,001
Growth ,977
303,01
9 1 7 ,000
Exponential ,977
303,01
9 1 7 ,000
Logistic ,977
303,01
9 1 7 ,000
We can clearly see that due to low R square value and significance higher than 0.05
logarithmic and inverse function do have low predictability value and will be eliminated from
the further research. For the models the significance value of the F statistic is less than 0.05
for both models, which means that the variation explained by each model is not due to chance.
The R Square statistic is a measure of the strength of association between the observed and
model-predicted values of the dependent variable. The large R Square values indicate strong
relationships for models other than logarithmic and inverse. Predictability of S function also
remains questionable due to relatively low R square value.
Now let’s examine the remaining functions graphically:
54
Figure 4.4 Curve fit analysis for Linear, Quadratic, Cubic and Power functions
4000,00
3000,00
2000,00
1000,00
0,00
PowerCubicQuadraticLinearObserved
Mobile_subscribers_thous
From the Figure 4.4 we can visually observe that only Cubic and Power function matches the
observed patters of cumulative number of cellular phones subscribers in Lithuania.
Now let’s look at the remaining functions:
55
Figure 4.5 Curve fit analysis for Compound, S, Growth, Exponential and Logistics
functions.
4000,00
3000,00
2000,00
1000,00
0,00
LogisticExponentialGrowthSCompoundObserved
Mobile_subscribers_thous
We can clearly see in the Figure 4.5 that all the models, with only exception of S function,
matches the observed behavior.
So after the examination of the function parameters and graphical curves fit we have
following function for future consideration: Cubic, Power, Compound, Growth, Exponential
and Logistics functions.
Now let’s examine exponential functions as it was done in the Botelho, Pinto (2004) research.
Table 4.17 Estimation results of the exponential model
Model Summary Parameter Estimates
Equation R Square F df1 df2 Sig. Constant b1
56
Exponenti
al ,977 303,019 1 7 ,000 46,828 ,492
As in Botelho, Pinto (2004) research estimation results of the exponential growth model
(Table 4.17) demonstrates that it fits the data very well, with high coefficient of
determination (0,977), for the estimated parameters.
Model predicts that the number of cellular phone subscribers is growing at the rate of 64
percent per year (13The coefficient estimate 0 ,492 shown in Table 4.3 is the estimated
instantaneous growth rate. The corresponding quarterly growth rate is computed as (e ^0.492 -
1) =0.6335). This is very credible prediction, because average growth rate of Lithuania mobile
phone subscribers for the period was 75% per year. Unlike the Botelho, Pinto (2004) we can
say that exponential growth can be successfully used for predicting the growth rate in
Lithuanian market.
Concluding the empirical analysis we can say that:
1. In the correlation analysis only the correlation between mobile phone subscribers and
GDP demonstrates perfect results with very high coefficient of correlation and
acceptable significance level. Two other correlations with average income and fixed
line subscribers also demonstrate the relationships, but they are on the margin of
statistical significance.
2. In the single regression analysis also the relationship between mobile phone
subscribers and GDP is statistically significant, with all other combination being rather
far from statistical significance level. As for the multiple regressions no model
demonstrated the statistical significance.
3. Curve estimation analysis reveals that several functions can depict the growth of the
number mobile phone subscribers. However exponential model, also used by Botelho,
Pinto (2004) demonstrates needed statistical significance and can be used for the
prediction.
57
Conclusion The main idea of this master thesis is to examine the growth and development of the mobile
communication industry by trying to find, analyze and evaluate the factors which had most
significant impact on that growth.
From the brief overview of mobile communication technologies development in Section 1,
we can conclude, that like in most information technologies, advances in mobile
communications occur through a process of gradual evolutionary development and the
„jumps“. No clearly observable continues pattern can be formulated because it is very
difficult to evaluate the impact of continues evolution and impact of occasional bounce.
However currently we are witnessing development of only 3rd generation mobile
communication, while in the past we had one development of analogue and one development
of digital technology. It can be said, that after more generations of mobile communication will
developed throughout the years (discussion about 4th generation of mobile communication is
already happening) maybe it will be possible to find clearly observable pattern, characteristics
and objective parameters of evaluation.
In the framework considered in Section 2 concepts of both network externalities and diffusion
of innovations are interpreted in the light of current situation of mobile communication. It
appears that using network externalities reasoning mobile communication growth can be
sustainable explained. Especially this is true for the reasons of network externalities existence,
findings about technology adoption in industries where network externalities, features of
competing systems, features of incompatible systems. Also analysis of the authors which try
to interpret and renew traditional network externalities concepts gives additional insights.
As for the diffusion of innovation, Rogers (1963) segmentation of the potential adopters is
very useful and perceptive. This segmentation might be successfully applied in the market
researches of the mobile communication services providers, as different groups of potential
adopters with different characteristics might be seen as target groups requiring special
techniques of marketing. As diffusion of innovations concept is widely applied and developed
in the researches of the mobile communication growth, not much of the additional application
58
of the concept can be formulated. However application of diffusion concepts leads to the
empirical evaluation of growth which is very significant for such research.
Section 3 deals with the regulation of mobile communication sector discussing the methods,
the impact and strategies. The whole summary of the literature leads to the idea, that no single
best strategy for allocating scarce spectrum resource can be created. None of the discussed
spectrum license allocation methods is perfect, but most of them do have specific
considerations which might help to conduct the process efficiently. Of course before
considering any of the methods of spectrum allocation, many factors must be taken into
account to assure the effective outcome. Also it can be said that original and insightful
approaches to the problem might be extremely successful (case of Hong Kong). The most
important thing is that the chosen way of implementation of spectrum allocation should be
carefully researched with possible consequences on the operators itself and future customers.
The example of European 3G licensing demonstrates that short sighted thinking about initial
governed revenues might even lead to very serious consequences for the development and the
implementation of the technology.
Considering the Section 4 where empirical analysis of data was presented it can be said that
used statistical methods mostly confirm the theoretical presumptions about the factors
influencing growth of new technologies. As the distinction between economical and
technological factors influencing mobile communication development was made, both factors
groups demonstrated significant and observable impact. However due to relatively little data
available (some of the needed data are even uncollectible), not all the theoretical assumptions
might be tested and explained.
Finally following research ideas of Lithuanian mobile communication market could be
considered for the future researches:
1. Evaluation of impact of more specific economical factors, such as tariffs, price of
handsets.
2. Evaluation of more technological factors which also describe growth of other
information technologies (for example usage of computers, internet)
3. Usage of various regression forms and techniques involving various functions.
4. Comparative analysis of the neighboring mobile communication markets.
59
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