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

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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

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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.

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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.

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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.

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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

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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”

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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

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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.

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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

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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

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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.

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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.

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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.

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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

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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.

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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

20

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”.

21

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

27

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.

33

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.

34

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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