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    Master thesis from BI Master thesis from BI Document Transcript

    • INTERNATIONAL SCHOOL OF MANAGEMENT MASTER OF SCIENCE PROGRAMME Nerijus Dumbrava Student ID: 0786324 MASTERS THESIS TITLE OF THE THESIS Supervisor Nerijus Pačėsa 2005 _ _ Supervisor Lars Thue 2005 _ _ Reviewer 2005 _ _ Reviewer 2005 _ _ VILNIUS, 2005
    • CONTENTSList of Figures 2List of Tables 3Introduction 41. Overview of 1G, 2G, 2.5G, 3G mobile communication systems 71.1. First generation (1G) 71.2. Second generation (2G) 91.3. Intermediary generation (2.5G) 111.4 Third generation (3G) 111.5 1G, 2G, 2.5G, 3G compared 131.6 1G, 2G, 3G in Lithuania 142. Internal - structural factors influencing growth 152.1 Network effect 152.1.1 Michael L. Katz and Carl Shapiro 152.1.2 Stan Liebowitz and Steve Margolis 202.1.3 Other authors 212.2 Diffusion of innovations 232.2.1 Everett M. Rogers 232.2.2 Bass model 253. External – regulation factors influencing growth 293.1 Methods of allocation of scarce resources 293.2 Role of regulation 323.3 Alternative approach 344. Empirical analysis 374.1 Example of empirical research 374.1.1 Massini’s research 374.1.2 Botelho research 404.2 Empirical data analysis 434.2.1 Correlation analysis 454.2.2 Regression analysis 484.2.3 Curve estimation 53Conclusion 58References 60 1
    • LIST OF FIGURESFigure 4.1 Scatter diagram for mobile phone subscribers and GDPFigure 4.2 Scatter diagram for mobile phone subscribers and average income.Figure 4.3 Scatter diagram for mobile phone subscribers and fixed line subscribersFigure 4.4 Curve fit analysis for Linear, Quadratic, Cubic and Power functionsFigure 4.5 Curve fit analysis for Compound, S, Growth, Exponential and Logistics functions 2
    • LIST OF TABLESTable 1.1 Table 1.1 features of 1.5 1G, 2G, 2.5G, 3G mobile communication generation systemsTable 4.1 Cumulative data of mobile phone and fixed line subscribers.Table 4.2. Economical variablesTable 4.3 Correlation. Number of mobile phone subscribers and GDP.Table 4.4 Correlation. Number of mobile phone subscribers and average incomeTable 4.5 Correlation. Number of mobile phone subscribers and fixed subscribersTable 4.6 Regression coefficients. Mobile phone subscribers and fixed line subscribersTable 4.7 Regression characteristics. Mobile phone subscribers and fixed line subscribersTable 4.8 Regression coefficients. Mobile phone subscribers and GDPTable 4.9 Regression characteristics. Mobile phone subscribers and GDP.Table 4.10 Regression coefficients. Mobile phone subscribers and average incomeTable 4.11 Regression characteristics. Mobile phone subscribers and average income.Table 4.12 Multiple regression coefficients. Mobile phone subscribers, fixed line subscribers and GDP.Table 4.13 Multiple regression characteristics. Mobile phone subscribers, fixed line subscribers and GDP.Table 4.14 .Multiple regression coefficients. Mobile phone subscribers, fixed line subscribers and average income.Table 4.15 Multiple regression characteristics. Mobile phone subscribers, fixed line subscribers and average income.Table 4.16 Time series functions results.Table 4.17 Estimation results of the exponential model 3
    • INTRODUCTIONMobile 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 mobilecommunications users. Starting from early introduction in 1990, number mobile telephonesubscription has doubled globally every 20 months. Starting from an 11 million subscriberand an average penetration of 1% at 1990, the mobile communications industry now providesservices to 1,404 billion subscribers (International Telecommunication Union, 2003). As thenumber of mobile subscribers in some countries is already overtaking fixed line subscribersand reaching saturation level higher than 100%, it looks that mobile communication isbecoming primary mean of voice communication transmission However it appears, thatgrowth is likely to continue in the future taking even more interesting forms after theintroduction of technically advanced mobile cellular networks. This master thesis is intendedto 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 thesubject. Gary Madden, Grant Coble-Neal, Brian Dalzell (2004) suggest that one of thereasons of such impressive growth over last decade could be market situation itself, becausethe situation in mobile telecommunication sector from the early start was very different thanin the sector of traditional fixed line telecommunication. Authors remark that “Entry ofmobile providers into former monopoly markets ensured the evolution of a more competitiveenvironment.” From the very first mobile operators where operating in very competitiveenvironment, because usually in most countries at least two service providers where issuedwith the licenses. That’s is why operators had to employ the strategies based on highcustomers satisfaction: developing pricing packages to differentiate themselves from theircompetitors, isolate market segments and target specific customer groups and geographicregionsJha and Majumdar (1999) have find out that mobile telephony penetration is varyingconsiderably amongst different countries depending to their gross domestic product (GDP).Authors insist that bigger economical success translates into an superior demand for mobiletelecommunications services. 4
    • Gruber, Verboven (2001) notice that growth in mobile telephony might be affected by suchregulatory influences as the timing and number of licenses issued the method which is used togrant the licenses, timing and manner of technological standardization.However Erik Bohlin, Stanford L. Levin, Nakil Sung in the editorial of Special Issue onGrowth in Mobile Communications of Telecommunications Policy remark that “As is widelyrecognized, accurately predicting the evolution of demand for new applications of technologyhas eluded experts again and again. The history of mobile communications has beencharacterized by underestimation of overall demand, overestimation of the potential of certainapplications (such as WAP), and the failure to foresee the popularity of others (such asSMS).”But the authors also add that complicatedness of forecasting does not necessarily mean thatvarious interest parties in the mobile communications field cannot impact developments bycreating environment for expansion, meaning that it main concern should be put onidentifying and removing concrete barriers to continues growth. Balance should be foundbetween the requirements of consumers and demands businesses, the availability ofinfrastructure and development of services, the financial equilibrium of operators, equipmentdevelopers and other players in the mobile wireless area and the control employed by variousregulatory bodies.Development of technologies ads one more interesting feature to the picture – services of datatransmission over mobile devices under 2.5G and 3G systems are already offered in somemarkets. Already today there are many prediction and different opinions how these systemsshould work, how their services should be priced and how consumers will react. Author ofmaster 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 communicationnetworks. It is intended to provide information about standards, generations, and timeline ofthe evolution until current date and brief overview of mobile communication networksdevelopment in Lithuania. 5
    • Section 2 provides discusses of relevant theoretical concepts from theory of economics ofinnovations literature. To aid this discussion two most relevant subjects where chosen andwill be addressed: 1. Network effect 2. Diffusion of technological innovationMentioned concepts are studied having in mind their impact on researched topic thus linkingsome of the researcher’s theoretical findings with practical examples from mobilecommunication industry. Also these findings and concepts will be used in empirical part ofthe research.Section 3 will be used to address the role of state regulation by reviewing relevant researchesmainly about auctions of frequency distribution.Section 4 concludes the paper by performing empirical research of growth factors ofLithuanian mobile communication market. Existing researches on similar topic will bereviewed in this section as well.General focus research question: How has mobile communication industry developed tocurrent stage? Which factors and how influenced growth of mobile subscribers?Author assumes that following research questions with corresponding objectives should beaddressed in his research:Research question Research objective 1. How has mobile communication 1. To overview creation, main developed till now? principles and development of GSM standard 2. What implications, concepts and 2. Examine the theoretical ideas does literature provide about concepts of network effect and the possible reasons for growth diffusion of technological mobile communication? innovation and find possible links with the growth. 3. What is the current role of market 3. Overview the researches regulating bodies in fostering or examining role of regulation and decreasing the growth? methods of regulation. 6
    • 4. Compare quantitative analysis4. How main factors driving mobile results. communication growth can be captured? 7
    • 1. Overview of 1G, 2G, 2.5G, 3G mobile communication systemsBefore deeply analyzing driving forces of mobile communication development and its currentstate, it is appropriate to review mobile communication technical development timeline, notforgetting first and second generation systems. Like in most information technologies,advances in mobile communications occur through a process of gradual evolutionarydevelopment and the “occasional quantum-leap forward” (Clint Smith, 2001) periods. Thisdevelopment is also characterized by term “hype cycle” by Gartner researchers1. That’s why itis important to review development of mobile communication systems and try to observepatterns of the possible past hype cycles, which might be a useful tool for the predictions andforecasts about upcoming generation of mobile communication systems. Also beforeanalyzing such a complex and dynamic industry as mobile communication, it is important tohave some knowledge about basic technical details to better understand what is theenvironment of the industry in which decisions are made. 1.1. First generation (1G)Most of researchers, analyzing development of mobile communications systems, mentionUSA as a place, where mobile communication technologies where born. First successfulimplementation of the trial system was conducted in Chicago in 1978. The system was basedon technology called Advanced Mobile Phone Service (AMPS) and was operating at 800MHzfrequency. Commercial launch of this system was delayed and took place only in 1983.However other countries were also making significant progress. Japan has launched acommercial mobile communication system in 1979. System was based on the same AdvancedMobile Phone Service platform which was tried in the USA in 1979. In 1981 the firstEuropean mobile communication system was launched in Norway, Sweden, Denmark, andFinland 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. Laterversion of the system was working in 900MHz band and was named NMT-900. System waswidely recognized as a successful project and later was installed throughout Europe, Asia, andAustralia.1 8
    • Britain introduced at that time has chosen to adopt its own technology which was called TotalAccess Communications System (TACS) and took place in 1985. Actually TACS wasupgraded version of AMPS, which was installed in USA and Japan.In a few years time many other developed countries followed along joining the growingcommunity of mobile communications users, and soon mobile communications servicesspread across the continents. During that time several other technologies were developed, butAMPS, TACS NMT450 and NMT900 were the most successful and most widely adaptedtechnologies. 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, theyexceeded potential capabilities of the systems, especially in the highly populated areas. Due tospecific architecture of the systems, it was already impossible to assure good quality of theservice and it was clear that actions should be taken immediately. This lead to thedevelopment of second generation systems (2G). 1.2. Second generation (2G)Year 1982 could be called a virtual starting point for the development of second generationsystem, because in this year the Conference on European Posts and Telecommunications(CEPT) embarked on developing new generation mobile communication system byestablishing a group called Group Spéciale Mobile (GSM, which later become an acronym forsecond generation standard). After conducting an early technical work of the new digitalstandard, work was overtaken by the newly created European Telecommunications StandardsInstitute (ETSI) in 1989. ETSI finalized the first set of technical specifications giving thesame name of an earlier standard developer’s group– GSM.The first functioning GSM network was set up in 1991, followed by several more launched in1992. Also international roaming between the various networks soon followed. GSM wasregarded as hugely successful project and within few years almost all countries in Europebetween 1992 and 1996 set up GSM service, followed by countries in other continents. Itbecame evident that GSM will be more than just a European project - it was fast becomingglobal, by changing the meaning of GSM letters to “Global System for Mobilecommunications” 9
    • Consequently, the letters GSM have taken on a new meaning—Global System for Mobilecommunications.After few years one more important enhancement followed. Originally, GSM was designedto operate to use 900MHz band, but to increase network capacity 1800MHz band was addedto the standard, called DCS1800 since which function simultaneously with the old frequency.New sets of handsets where developed to support both frequencies. Nowadays most handsetssupport 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. EncryptionMost of the mentioned points directly benefit operator of the wireless system, but benefitswhich 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 ofthese reasons will be addressed later in this thesis), but one important detail should bementioned above all of them. GSM was highly technically advanced technology (and still is),because system design was made from the scratch without providing backward compatibilitywith 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 2Gwith 3G which is still under development. 2.5G basically is the method by which existingcellular operators are migrating into the next generation wireless technology, which is 10
    • extensively specified the International Mobile Telecommunications-2000 (IMT-2000)specification. For the implementation of 2.5G, there is no need to build totally new network,because services are provided by upgrading current 2G equipment. This means that 2.5Goffers a backward compatibility which is extremely important having in mind hugeinvestment 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 SwitchedData (HSCSD)• Enhanced Data Rates for Global Evolution (EDGE)2.5G gives the wireless operators a possibility to provide digital high speed data transmissionservices prior to the availability of 3G platforms. Providing 2.5 services, before 3G is verybeneficial 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 providesOn the other side, (Collins, 2002) mentions following challenges for the operators engaging inproviding 2.5G services:1. No one specific standard chosen for transition.2. The overlay approach3. The introduction of packet data services4. The new user devices required5. New modifications to existing infrastructure1.4 Third generation (3G)The demand for the next generation mobile communications technology became observableduring 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 connectionenables them to perform variety of different activities which prior where hardly possible oreven imaginative. Adding feature of mobility to the high speed access point would widen thepossibilities of high speed data transmission services even more. 11
    • Recognizing that fact, The International Telecommunications Union (ITU) in the 1990’slaunch the initiative called Future Public Land Mobile Telecommunications Systems(FPLMTS) which was meant to prepare recommendation for the next generation mobilecommunication systems. In 1997 ITU presented the recommendations under the name“International Mobile Telecommunications—2000” (IMT-2000) which gave general directionfor the development of 3G mobile communication systems.The IMT-2000 recommendations were intended to be unifying specification, enabling mobilehigh-speed data services using one or several radio channels based fixed network forproviding 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 trafficAs these where only general recommendation, ITU announced that it is open for thesubmission of technical 3G implementation proposals. After these proposal where submitted,5 technologies for terrestrial service where announced:• Wideband CDMA (WCDMA)• CDMA 2000 (an evolution of IS-95 CDMA)• TD-SCDMA (time division-synchronous CDMA)• UWC-136 (an evolution of IS-136)• DECT 12
    • 1.5 1G, 2G, 2.5G, 3G comparedFollowing 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 systemsGeneration Standards Features Speed Band Analogue voice service, 300MHz -1G AMPS No data service 600MHz Digital voice service; Low speed data transmission; CDMA,TDMA, Enhanced calling 9.6K - 14.4K 600MHz -2G GSM,PDC features, caller ID; bit/sec 1.8GHz Voice mail; Short messages; Global roaming Phone calls/fax; Send/receive medium size email messages; GPRS, EDGE,2.5G Web browsing; 64-144kb/sec HSCSD, Navigation/maps; New updates; Multimedia messages 1.5GHz - Send/receive large email 3GHz WCDMA, messages; CDMA2000, High-speed Web; 144kb/sec-3G TD-SCDMA, Navigation/maps; 2mb/sec UWC-136, Videoconferencing; DECT TV streaming; Electronic agenda 13
    • 1.6 1G, 2G, 3G in LithuaniaFirst 1G generation mobile communication NMT-450 licence in Lithuania was issued1992.06.04 by Lithuanian Communication and Transportation Ministry to the joint stockcompany of Lithaunia and Luxemburg „Comliet“. Licence was issiued for 10 years period forthe price 884,000LTL. For the next 3 yeras „Comliet“ was leading mobile communicationoperator in Lithuania with network covering about 80% of state territory. However with theintroduction 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 1994.10.25by Lithuanian Communication and Transportation Ministry to the Joint Stock Company„Litcom“ which was later renamed to „Omnitel“. Currently „Omnitel“ is leadind mobilecommunication network operator.Second 2G mobile communication GSM DCS 900 licence in Lithuania was issued1995.05.09 to the Joint Stock Company „Mobilios telekomunikacijos“ which was laterrenamed to „Bitė GSM“. Currently „Bitė GSM“ is one of the 3 mobile communicationoperators 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 withthe second licence on 1997.10.31. Each of the licences was issued for 10 years with fee884,000LTL.1998.02.23 proposed a tender (based on „beauty contest“ model which wil be examined withmore details later in the master thesis) for GSM DCS-1800 licence.1998.09.23 thee winnerswhere anounced: „Omnitel“ , „Bite GSM“ , „Levi and Kuto“. Joint Stock Company „Levi andKuto“ was later renamed to Tele-2. Tele-2 currently is second bigest mobile communicationnetwork operator in Lithuania with market share.. 2000.12.29 „Tele-2“ was issued with theDSC 900 licence. All mentioned licences tradionaly issued for 10 years period for 884,00LTLfee. 3G licences in Lithuania are currently not issued. 14
    • 2. Internal - structural factors influencing growthIn the following section various sources of network effect literature are summarized. For themaster thesis two main groups of authors where chosen: traditionalists, which starteddeveloping network effect concepts in the 5th – 6th decade of the 20th centaury and fewrelatively young and modern authors from the 9th decade of 20th centaury who are currentlytrying to renew and supplement traditional concepts.2.1 Network effect2.1.1 Michael L. Katz and Carl ShapiroIt seems rather logical to start analyzing and reviewing literature on network effect from themostly 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 describednetwork effect in a fashion which is commonly used today. Katz & Shapiro (1985) observethat “there are many products for which the utility that a user derives from consumption of thegood increases with the number of other agent consuming that good”. Authors of the paperwere one of the first to give reason for positive consumption externalities arising in thenetwork. In the following section author of the master thesis intends to relate these reasonswith 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 servicebecomes (in case of mobile telephony, as number of mobile phone users increases, theincentive 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 providemore and more additional services (for example SMS, fax, email, MMS) which are alsoincentive to join. In case of 2.5G and 3G these services can be provided not only by networkoperators, but by external service providers, which will mean even more additional andattractive services and applications.3) Post purchase service development. 15
    • As current mobile communication networks in most countries do have full coverage of thearea, 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 providedby other retailers which are not connected to network operators. This also has big impact aspositive network externality for mobile communication market.Also Katz & Shapiro (1985) state that “For communication networks, the question is one ofwhether consumer using one firm’s facilities can contact consumers who subscribe to theservices of other firms”. This is applicable to mobile communication networks, becausecurrent networks are interconnected nationwide and worldwide with global roaming system.But also we should not forget that international calls are still relatively costly service, whencompared to local calls. It’s rather obvious that in the future cost of international shoulddecrease 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 significantlydifferent technological environment, it seem that it’s findings are universal and can areapplicable today.Katz & Shapiro (1986) also provide us with the findings about technology adoption inindustries where network externalities are significant. Author of the master thesis intends tolink some of these findings with relevant examples about the current situation in the 3rdgeneration mobile communication industry, where 5 incompatible standards (WCDMA,CDMA2000, TD-SCDMA, UWC-136, and DECT) for possible technology adoptation arepresent: 1. Compatibility tends to be undersupplied by the the market, but excessive standartization can occur. This is very the case for a current situation of technology adoptation in 3G phones market. Despite the fact, that 5 major 3G standarts are present, major mobile phone producers (Nokia, Motorola, Sony Ericsson) are revealing handsets for only WCDMA standart, as most network operators which are starting to provide 3G services have chosen WCDMA platform for the current implementation of 3G. This does not mean, that other standarts will be forgotten (maybe they are under development or will be developed for the future 3G), but up to date WCDMA has established itself as a leading 3G standart. 16
    • 2. In the abscence of sponsors, the technology superior today has a strategic advantage and is likely to dominate the market. According to this finding, WCDMA is superior today even without major sponsoring activities and will dominate 3G market in the nearest future. This seems rather likely, because if the WCDMA technology will work as it is intended and will assure qualitative services according to 3G specifications, there will not be much incentive for network operators to invest in the creation of the networks based on other competing standarts. 3. When one of two rival technologies is sponsored, that technology has a strategic advantage and may be adopted even if it‘s inferior. This might be applicable to WCDMA case – other competing standarts might be have technical implementation advantages, but as WCDMA was chosen by most important network components and handsets market players (for the reasons which remain unknown), it is cleat that this technology has a strategic advantage. 4. When two competing technologies both are sponsored the technology that will be superior tomorrow has a strategic advantage. In the 3G market is also applicable, because major network equipment and handset producers might have chosen WCDMA as a leading standart, because they see that WCDMA technological capabilities assure it‘s ussage in the future.Katz and Shapiro (1994) also examine network effect in the presence of systems competition.First of all let’s look at how authors of the paper define concept of systems: “Many productshave 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 componentstogether with an interface that allows the components to work together.” Authors alsoseparate two types of defined systems: • Communication networks. • Systems based on hardware software paradigm.Communication systems allow various users exchange specific type of messages when theyjoin the system, which provides the “interface”. Interface is usually created and owned byservice provider and a tool, a component to access the network might be property of user or 17
    • provider. Obviously in case of telecommunication, that interface is network and componentsare the phones. Concept of systems might define fixed telephony as well as mobilecommunication industries.Software - hardware paradigm systems both interface (hardware) and value providingcomponent (software) are usually purchased by the user (for example PC hardware andsoftware or CD player and audio CD’s). In some cased “hardware” might be sold under itsproduction costs and producers get its revenue from the sales of “software” (for exampleMicrosoft sells Xbox gaming console for ~50% of its production costs).Typically both types of systems demonstrate observable presence of network effect. In case ofcommunication network systems “value of the membership to one user is positively affectedwhen another user joins and enlarges the network”. This is rather usual description which cantraditionally be found in most research papers of Katz and Shapiro. But network effect inhardware software paradigm network is described differently: consumers form the expectationabout which systems are going to be popular and by buying software they encourage theproducers to achieve economies of scale, which is also specific type of network effect (valueobtained from the network increases as more users join).Also authors point out three main features of systems competition: • Expectations • Coordination • CompatibilityRational expectations users form expectations about availability, price and quality of thecomponents they will be buying in the future. This effect is more obvious in case of softwarehardware paradigm, because here users purchase “interfaces” themselves, but effect can beobserved in communication systems as well. In communication systems switching costs alsoexist – usually access to the network is not granted for free, for example mobile phone usersmust 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 bothtype’s systems components and interface production must coordinate their action with othercomponents and interface producers as well. It is rarely the case when one producer cansuccessfully produce both interface and components of the system. Also the significant rolethere is played not only by the market forces, but also by various industry-wide standardsetting bodies. The impact of standard setting bodies in case of mobile telephony wasaddressed in first part of the master thesis. 18
    • Issue of compatibility between systems is also addressed by the authors, but in rather originalmanner. Katz and Shapiro (1994) reject the idea that “incompatibility is just anothercoordination failure”, and claim that “obtaining and maintaining compatibility often involvesa sacrifice in terms of product variety or restraints on innovation”. This idea could be usedwhen analyzing two different cases of introduction of new generation mobile communicationsystems, because Katz and Shapiro (1994) pointed out that ”Incompatible systems also canrepresent different generations of a single core technology”. So when second generationmobile communication “interface” was introduced, it’s components (mobile phone) wherenot compatible with first generation standard interface, but currently third generation mobilephones are usually compatible with the existing second generation networks interface. Fewreasons 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, whilesecond and third generation systems are compatible. But according to incompatible systemsdefinition by Katz and Shapiro “two communication networks are incompatible if subscriberson one network cannot communicate with those on other networks”, we could say that anygeneration mobile communication generation users can reach any other generation users byvoice calls; moreover they can communicate with totally different system – fixed telephonynetwork. Author of the master thesis would suggest update the concept of incompatibility infollowing way “two communication networks are incompatible if subscribers on one network 19
    • cannot communicate in any existing network interface using any existing networkcomponent”, because as in this case systems might look incompatible, but actually theirinterfaces are be linked. 2.1.2 Stan Liebowitz and Steve MargolisTwo of these modern authors, who question „classical“ network effect paradigms and searchfor more applicable models are Stan J. Liebowitz and Stephen E. Margolis.In their network externalities and network effect definition, included as entry in „The NewPalgraves Dictionary of Economics and the Law“ authors primarily are trying to separatewidely used concepts of „network effect“, „network externalities“ and define them moreprecisely:„The enthusiasm for recognizing and understanding these phenomena should not, however,lead us to inappropriate or premature conclusions. As we have noted above, there aredistinctions and reservations that ought to be maintained. The first and broadest is thatbetween network effects and network externalities. A further distinction is between pecuniaryexternalities and real ones. Even for the set a real externalities, it is important to note thedistinction between the problem of network size and that of network choice, the boundednessof the network effect, the likely symmetry of network effects for alternative products, theability of large consumers to self-internalize network effects, and differences in tastes.Liebowitz and Margolis (1995) explain term “network effect” in following manner: “Thecircumstance in which the net value of an action (consuming a good, subscribing to telephoneservice) is affected by the number of agents taking equivalent actions will be called a networkeffect” Author claim, that term “network externality” should be used to describe specific kindof network effect, where “equilibrium exhibits unexploited gains from trade regardingnetwork participation”.People when making a decision about joining a specific network (for exampletelecommunication or computer operating system) always inevitably consider how theirparticipation will affect others and how the participation of others will affect us, meaning thatpeople 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” inthe research literature was always associated with positive effect, but that is only one part ofthe picture. Negative effects caused by network externalities are also a part of our lifes, forexample 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 theconsumption. Also there are network, where willingness of other people to join the networkharm each others interests – for example excessive demand for housing in particular areacauses price bubble thus making people pay more than they initially expected. Thisunderstanding of network externalities expands areas of definition usage significantly whencompared to the definition of Katz and Shapiro.Liebowitz and Margolis (1995) summarize that “goods exhibit network externality whereverthe consumer enjoys benefits or suffers costs from changes in the size of an associatednetwork, that is, changes in quantities demanded”. Authors notice that benefits and costsresulting in such situation are directly connected to compatibility, brand familiarity, productinformation, status, service availability or the prices of network related goods.Also Liebowitz and Margolis (1995) add one more interesting dimension to the Katz andShapiro classification of networks of communication networks and networks based onhardware – software paradigm (discussed earlier in the master thesis). Authors also classifynetworks according to the ownership of network itself. They notice that in the communicationnetworks “participants are literally connected to each other in some fashion”, where networkcreation requires investment of capital and property rights are always established for suchnetworks. In such networks users join only with the permission of network owner and usenetwork according to provided rules. Obviously mobile communication networks belong todescribed category. Other type of networks, which correspond to Katz and Shapiro hardware– software paradigm model, are named by Liebowitz and Margolis as "metaphoricalnetworks" which are described as providing interrelationships in with no physical connectionsused. These networks are not likely to have an owner, because usually it is not possible tohave one. Example could be drivers of particular car brand. 2.1.3 Other authorsDespite the fact that Michael L. Katz and Carl Shapiro works on network effect and networkexternalities are widely acclaimed as classical, after some time other authors are tryingcritically review all the findings which others authors have made and explain areas of thetheory, which are not completely covered or lack real life evidence. Master thesis authorintends to include few example of such critical approach; one of them is Tim Weitzel, OliverWendt, Falk v. Westarp recent paper “Reconsidering network effect theory”. 21
    • The authors claim that: “While the traditional models greatly contributed to the understandingof a wide variety of particular (macroeconomic) problems associated with the diffusion ofstandards, they fail to explain the phenomenological variety of diffusion courses in today’sdynamic information and communication technology markets.“Authors remark that current network externalities theory does not cover the heterogeneousproperties of the markets with new products such as digital television, cellular phones, officesoftware, Internet browsers, EDI-solutions. These are the markets and products, which wherenot present during the time when “classical“network externalities where examined and needfurther researches.In the article, authors try "Systematically reveal deficiencies in the models of positive networkeffects by analyzing common assumptions and conclusions, before extending this criticism tothe more general premises of the neo-classical framework.“ 22
    • 2.2 Diffusion of innovationsAccording to Encyclopædia Britannica2 diffusion of innovations definition:“Some social changes result from the innovations that are adopted in a society. These caninclude technological inventions, new scientific knowledge, new beliefs, or a new fashion inthe sphere of leisure. Diffusion is not automatic but selective; an innovation is adopted onlyby people who are motivated to do so <…> Many innovations tend to follow a pattern ofdiffusion from higher- to lower-status groups.”Also mentioned higher status is defined as “young, urban, affluent, and highly educated, witha high occupational status. Often they are motivated by the wish to distinguish themselvesfrom the crowd. After diffusion has taken place, however, the innovation is no longer asymbol of distinction. This motivates the same group to look for something new again.”2.2.1 Everett M. RogersEverett M Rogers in his book “Diffusion of Innovations” defines the diffusion of innovationsprocess saying that innovation is usually communicated “through certain channels over timeamong the members of social system”. This means that four key elements are part of thediffusion 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 processwith 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 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 mobilecommunication looking at the markets where mobile communication gradually establisheditself going through all technical generations. Innovators where the first users, usuallyfinancially unrestricted and interested in new technology, who purchased first extremelyexpensive mobile communication sets and paid very high price for the services. At this stagemobile communication was very niche market. After that, the wave of early adopters followedwhen network coverage became larger. It was mostly institutional and business users. Theonly distinction between the early majority and late majority in this case could only be thedifference of financial abilities, because after the technology has advanced, it was only aquestion of time when it will became cheaper and will be accessible to most people in mostcountries. This segmentation of course is relevant in the countries where mobilecommunication was introduced gradually, because countries which adopted alreadydeveloped mobile communication technology (for example developing countries adoptingnetworks of 2G) are in little different situation, when users adopting the technology do nottake any adoption risk. The technology is already tried in other countries and they potentialadopters know technology usability. This means that in such countries only two major groupscould 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 fivestages 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 modelMahajan and Muller (1979) claim that objective of a diffusion model is to present the level ofreach of an innovation among a potential adopters over time.Moreover the rationale of the diffusion model is to show the successive increases in thenumber of adopters and forecast the continued advance of a diffusion process already inevolution.In the product innovation perspective, diffusion models focus forecasting first-purchase salesof innovations. They also might serve for the development of product life cycle. Mahajan etal. describe diffusion models as “first-purchase models” assuming that in the product lifecycle timeline there are no repeat buyers (this means that number of buyer is equal to thequantity of sold product).The best known and most basic first-purchase model of new product diffusion was defined byFrank M. Bass (1969).Main idea of the Bass model lies in assumption that potential adopters of any innovationreceive influence influenced by two means of communication: • mass media • word of mouth 25
    • Basically model assumes two groups of potential new technology adopters take place in thediffusion process. One group receives influence only by external influence channels – massmedia communication. Another group is a subject of internal influence – word of mouthcommunication. Bass names the externally influenced group "Innovators" and the internallygroup "Imitators". It is important to observe that the role of the groups in the diffusion ofinnovation process differs by the timing of the involvement. Innovators are first to receiveinformation about new product and do have financial means t purchase it and ability to useindependently. This is rather similar to the definition of Rogers (1963). However imitators areinfluence by personal connection with the innovators and in this manner the diffusionhappens. Bass model conceptual structure is graphically depicted Figure 3.1. Two curvesdepict noncummulative dynamics of technology adoption by making difference betweenInnovators and Imitators. Despite the assumption that innovators are usually the earlyadopters, 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 thecurve of internally influenced users reaches its peak. Also one more questionable assumptionof Bass model is observed by Mahajan et al.Technology adopter distribution assumes that an initial pm (a constant) level of adopters buythe product at the beginning of the diffusion process. Once initiated, the adoption process issymmetric with respect to time around the peak time T* up to 2T*. That is, the shape of theadoption curve from time T*to 2T*is the mirror image of the shape of the adoption curve fromthe beginning of the diffusion process up to time T*”. Usually it can be observed that after thenumber of adopters reaches peak, market saturation level is not far away. This means thatafter 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 tis expressed by the following function:n(t) = p + qWhere p is a number of innovators (which is calculated by knowing “coefficient ofinnovations” – proportion of the potential innovators within population) and q number ofimitators (which is calculated knowing “coefficient of innovators”. Time dimension by havingdifferent 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 extensivelyquoted, interpreted and expanded in the innovations literature during last three decades. Fewinteresting extensions of the model are provided below.Tanny and Derzko (1988) imply that concepts of „Innovators" and "Imitators" used in Bassmodel do not precisely describe characteristics of buyers taking part in the technologyadaptation process.They offer an addition of the model in which all potential adopters are divided in twodistinctive groups which they label “Potential Innovators” and “Potential Imitators”. PotentialInnovators as well as Potential Imitators are a subject of the mass-media communicationinfluence, but only Potential Imitators are influenced by word of mouth, where PotentialInnovators are free of this influence. This seems really reasonable, because Potential Imitatorsalso receive also receive external mass media influence, but it might be, that this influence hasdifferent 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 mediaprovided information (for example people with more financial resources do have access betterquality information channels – satellite TV, international press).One more interesting observation was made by Mahajan et al. (1990). A key characteristic ofthe Bass model is that it addresses the market in the aggregate manner, measuring the numberof two groups adopters who acquire the product in time period t. Mahajan et al. (1990) raisessubsequent question: “Can the diffusion model be built by aggregating demand fromconsumers who behave in a neoclassical microeconomic way? That is, assume that potentialadopters are smart and are not just carriers of information. They therefore maximize someobjective function such as expected utility or benefit from the product, taking into account theuncertainty associated with their understanding of its attributes, its price, pressure from otheradopters to adopt it, and their own budget.”This means that every single adopter both from imitators and innovators groups do have morecharacteristics that it is taken in to account in the Bass model. That it is why one moredimension should exist – it is probability of adopting the product in time t for consumer withits specific characteristics. Mahajan et al. mentions several parameters which could be addedto the Bass model: individual uncertain perception of the innovations performance, predictedfuture 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 utilityfor the innovation becomes greater than the status quo (he is better off with the innovation)”. 28
    • 2. External – regulation factors influencing growthIn this section three articles three relevant articles are analyzed. John Kruse article discussesthe methods of conducting spectrum allocation. Harald Gruber article analyzes the role of theregulation in the process of growth. This article provides us with the alternative example ofspectrum allocation in Hong Kong.3.1 Methods of allocation of scarce resourcesThe study of Jorn Kruse (2002) examines various methods and options available for theallocating scarce spectrum resources. Brief summary of the discussed methods is providedbelow.Kruse (2002) already in the introduction mentions that current developments of the mobiletelephony market can be characterized by low level of regulation in most countries, only withsignificant exception of spectrum licensing.Also he ads, that spectrum allocation methods by large shape industries and market structuresin the individual countries and as spectrum is a scarce resource, it is very important thatallocation 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 ofmore or less spectrum (and what kind of spectrum) is of primary importance for the economicsuccess of a mobile operator. Adequate spectrum allocation is a crucial factor, if a mobilesector 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 numberof the markets players, standards, competition rules etc.Also author ads that spectrum can be described as an essential resource and the allocation ofspectrum is really significant for the level of competitiveness of operator and has an impact onthe competition and the efficiency of the whole industry.Kruse (2002), before going into details of various spectrum allocation methods, explainsimportant the concepts of Intramodal and Intermodal Spectrum Allocation. 29
    • Term Intramodal Spectrum Allocation is used to describe the rivalry between similar servicesoperating within the same frequency thus creating the situation when spectrum becomessubject 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 spectrumdimension is traditionally decided by regulating body. However in the literature ideaspromoting the free market standardization process can be found.Following methods for Frequency Resources Allocation are used by various countriesregulating 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 tothe 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 ofcollusion exists. By saying that Kruse (2002) remarks that most of European spectrumauctions 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). 31
    • Kruse (2002) concludes the discussion about spectrum allocation mechanisms by stating that“In general, economists strongly advocate auctions. But in practical terms, this very muchdepends 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 objectiveshould not be the maximization of revenues, but “The proper functioning and efficientoutcome of the mobile markets are supposed to be the main objectives for the spectrum andlicensing auctions”. European 3G licensing will be discussed in more details within thisthesis.Author also suggests questions which should be taken into account when developing aspectrum 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 regulationHarald Gruber (2000) in his articles tries to answer the question what impact does the scarcityof 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 withmost important developments during the existence of spectrum allocation period. Briefsummary of the most relevant points of the article is provided below.Author starts the article by emphasizing the importance of spectrum allocation in nowadaysmobile communication industry. The competition for the scarce frequency resources startedwhen first non monopoly operators where allowed to act as service providers. Despite the factthat mobile telecommunications industry is constantly improving the spectrum efficiency usedfor the services, the increasing popularity of mobile telecommunications services puts onadditional pressure for allocating even more frequency spectrum. Also as mobile 32
    • 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 communicationwas seen as a natural monopoly, for the rational reason, that due to technically not advancedsystems, frequency spectrum availability was very scarce with poor efficiency of resourceusage. But regulators and other public bodies became more and more concerned that theabsence of competition in the industry leads to inadequate incentives to decrease costs andalso provides no suitable environment innovation with observable under-investment.Frequency competition was born after the introduction of more advanced second generationmobile communication systems, when some countries started issuing more than one licensefor the providers of mobile communication services. After that the idea of fees for issuing thelicenses came into place. Thus mobile communications industry became the first major spaceof competitive supply of telecommunications services.Gruber (2000) claims that government licensing policy in mobile telecommunications hasfollowing 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” allocationmethod in Europe. Also in some countries incumbent fixed line telecommunications operatorswhere granted with the licenses. However it was obvious that it can be only a temporarysolution, because as the market labialization was increasing (mainly due to impact of theintereueopean regulation) competitive interaction among bidders was constantly increasing.Gruber (2000) provides reflection of these trends using statistics about various countriestelecommunication sector: from 118 countries which adopted first generation mobilecommunication systems 75% had monopoly, while from the 87 countries which adoptedsecond generation mobile communication systems 56% already had oligopoly.Interpreting that Gruber (2000) makes an implication that “Capacity thus seems to be a firstcrucial 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 earlyyears), the effects of competition on mobile penetration are likely to be modest. The effects ofcompetition are potentially much larger under the digital technology when capacityconstraints are relaxed.”Gruber concludes the article by saying that the scarcity of frequency spectrum is still themajor concern of the mobile telecommunications industry. Despite the significanttechnological progress that industry made during last decade, scarcity of the spectrum still haslimitation for the providers of the services. 3.3 Alternative approachThis research paper of Xu Yan (2004) is reviewed in this master thesis as unique andsuccessful example of alternative approach of spectrum allocation. Hong Kong‘s spectrumregulating body Office of Telecommunications Authority after intensive debate andconsultation formulated 3G licensing scheme, which is very different from the traditionalspectrum licensing auctions used in Europe. Brief review of these differences and steps whichwhere taken upon reaching the unique solution is provided flowingly.In the beginning of the research paper Xu Yan (2004) discusses difficulties, risks andconstraints 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 thedifficulties of formulating a harmonizing licensing framework primarily are caused bydifferent economic backgrounds and existing spectrum allocation situation which determineaspects of future spectrum allocation.Analyzing the mentioned economic backgrounds and existing spectrum allocation situationXu Yan (2004) remarks that Hong Kong government (unlike some governments in EU) hasno need to raise significant revenue through spectrum auctions and can trade spectrum atrelatively 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 socialbenefit much higher than the commercial benefit and will produce significant externalities ifany unfavorable situation developed. That‘s why government should take actions to reducethe risk of potential investors. The most appropriate for the government for reducing the riskis reducing operator’s financial risks by using convenient license pricing.Knowing that Office of Telecommunications Authority eventually rejected primary proposalof auctions and started the discussion about how the spectrum allocation process might beconstructed. 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 complicatedissue in Hong Kong, because most of the 3G defined spectrum has not been occupied by otherservices and four 3G licenses can be issued.However four 3G licenses is relatively small number taking into account that where issued 11licenses in Hong Kong 2G market. The idea of the proposed solution how to deal with thisproblem came originally from Europe – model very similar to Mobile Virtual NetworkOperator concept (which exists in EURO) was offered. Mobile Virtual Network Operator isbased on the idea that some operators which do not have assigned radio spectrum can use theproportion of spectrum of original Mobile Network Operators and be allowed to build andoperate parts of the networks which do not require the use of radio spectrum. When thissolution is used Mobile Virtual Network Operator are able to offer 3G services using theirown brands but not operating the radio networks, thus reinforcing the competitive marketmodel even more.It was proposed that successful bidders which will be granted with the licenses will have theobligation to open at least 30% of their 3G network capacity for usage of non-affiliatedMobile Virtual Network Operator companies and/or content providers.Concerning the pricing of licenses it was decided that price of the license should be negotiatedcommercially. Only If commercial negotiation are not successful, the regulator has the right tomake own determination based on principles of fair interconnection, thus finding a balancebetween to low price (‘‘free-rider’’ phenomenon) and a sufficient investment return on costof 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 mentionedreturn 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, financialcapability. Moreover very original license pricing scheme was introduced. Bidders whereasked to bid for a level of annual royalty (as a percentage of turnover) from 3G servicesnetwork operations. However for the first 5 years of operations, minimum royalty paymentwill 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 anexisting 2G operators.Starting from year six to the end of the license period, 3G Mobile Network Operator will payroyalties to the Government according to the royalty percentage which is determined by theauction. The calculate actual royalty payment of course will different for different MobileNetwork 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 theopen network obligation of 3G licensees has been fully debated before the licenses wereauctioned, and all bidders have been very well informed about the potential competitivenessof the 3G market. As a result, no bidders should be so nave as to unreasonably overbid thelicenses, especially in the context that several European licensees are suffering from thewinner’s curse. “In this way government also is not losing either, because the continues royalty payments willallow the government strengthen possibly unstable future 3G services market with almost nofinancial risk. Hong Kong’s 3G licensing scheme might be seen a balance between scarcespectrum efficiency and working market competition thus providing an alternative way ofthinking about the problem. 36
    • 4. Empirical analysis4.1 Example of empirical research4.1.1 Massini’s researchMassini’s (2004) study investigates the diffusion of mobile telephony in Italy and UK usingthe 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 populationAuthor defines an aggregated diffusion model, based on the standard epidemic model, byintroducing economic variables.In addition to the economic variables authors also ads variable that takes into account theeffect of the technological change which occurred in mobile telephony during the researchedperiod. 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 Italyand the UK during the 1990s, using standard epidemic model and taking into account ofeconomic and technological factors that are helpful in explaining differences of the diffusionspeed. Also they suggest methods to differentiate long-run relationships and short-runadjustments of continuous diffusion.Interesting remark is that, two countries have very similar characteristics (size, wealth,population and geographical features), exactly the same mobile telephony servicesintroduction year (1985). Even so investigation market structures and institutional regulationframeworks, shows remarkable differences in two countries: Italy had near monopolysituation until the introduction of the second operator in 1995, while in the UK the servicewhere started from duopoly model which was enriched by the entry of a two more operatorsin 1994.Also author notices, that the strategies local mobile network operators employ, showsobservable differences in Italy and United Kingdom. Italian operators where primarilycompeting 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 handsetthus locking customers to their networks.This resulted in differing portfolios of clients in both countries with different tariff packagesin 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 contractcustomers and reduce the average revenue per subscribers, the success of prepaid services inItaly 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 wasemployed by the operators because of constantly increasing variety of products and serviceswhich ads substantial complexity and confusion when buying a mobile phone.Before conducting the empirical investigation, author extensively explains the diffusionprocess 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; thesecond force is decreasing number of potential adopters.Author also notices that “the epidemic feature is not simply spread of information about theexistence of the innovation, but also spread of information concerning its technologicalcharacteristics and the increase in post-adoption profitability”. This can be seen as a wisestrategy for the developers and promoters of any innovation: it is necessary to reduce thepotential adopters decision making process by reducing degree of uncertainty associated withthe 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 showstypical S-shaped curve both for Italy and the UK. Also author observes that numbersubscribers of analogue first generation technology reached its maximum saturation level bothin Italy and the UK in 1996, after which the decline follows.As it is typical for competing product generations the two technologies had showed samemarket shares in 1997, but in both countries analogue telephones started to disappear whiledigital telephones have grown even faster after that. 38
    • Also author remarks that from the point of view of the operators, the cost of the infrastructurefor the digital technology due to some specific technical reasons was lower and required muchsmaller investment when compared to the analogical technology. This means that serviceproviding 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 isonly a question of timeAuthor adds that:”The profiles of the subscribers of the analogue and digital technologiesfollow the characteristic pattern of technological substitution between successive generationsof technologies, where a new generation cannibalize the previous one and will becannibalized 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 researchThe recent paper of Botelho, Pinto (2004) paper analyses the pattern and rate of adoption ofmobile telephones in Portugal. Time-series data on the number of subscribers is analyzedusing statistically based method to estimate the market potential for mobile phones isemployed, allowing generation of confidence intervals about the estimated market potential.The nature of the used method allows researchers not only to empirically derive the expectedmarket potential, but also to determine the current stage of the market with respect tosaturation level and make observations about growth process.Botelho et al. (2004) estimated the S-shaped diffusion functions (exponential, Gompertz, andlogistic) growth models using time series data on the number of cellular phone subscribers) byordinary least squares method for exponential function and nonlinear least squares forGompertz 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. Recentmobile phone user’s statistics show that in some countries the saturation level has alreadyovercome 100%. This is possible because increasing proportion of population is using fewSIM 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 ofcellular phones in Portugal can be explained by S shaped -curve.Also he ads that “although both the Gompertz and logistic models describe a sigmoiddiffusion curve, it is only the logistic model that adequately describes the path of cellularphone diffusion in Portugal.”The reason for this is rather technical: the Gompertz model is derived from a skewedfrequency distribution, while the logistic model is based upon a symmetric frequencydistribution which fits researched situation much better. Also logistics model depicts the rateof 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.FunctionsBotelho et al. (2004) provides us with the specification of the traditionally used functionswhich depict S-shaped diffusion process. As author of the master thesis also performsstatistical data analysis similar to the one conducted by Botelho et al., overview of thesefunctions 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 ofmost innovations results from the fact that initially, during an embryonic phase, only a fewmembers of the social system adopt the innovation. Over time, though, an increasing flow ofnew adopters is observed as the diffusion process unfolds. This is the phase of rapid marketgrowth. Finally, during a maturing phase, the trajectory of the diffusion curve gradually slowsdown, 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 positivelyinfluenced by the number of existing subscribers and the difference between the saturationlevel and the number of existing subscribers.Mostly used functions of S-curves representing are the logistic, the Gompertz and exponentialfunctions. 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 fordetermining 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 curveis 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 analysisIn this section empirical data analysis using various statistical techniques is conducted.First of all correlation between number of Lithuanian mobile phone subscribers and economicvariable will be analyzed (GDP, average income). This should give an answer to the questionweather decision to become a mobile phone user is influenced by economical factors. Alsocorrelation number of Lithuanian mobile phone subscribers and the number of fixed linesubscribers will be employed, to answer the question weather the decision to become a mobilephone user is influenced by technological substitution factor.Secondly regression is employed also to test the relationship between number of Lithuanianmobile phone subscribers and mentioned independent variables (GDP, average income,number of fixed line subscribers). Additionally to the simple regression, multiple regressionmodels 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 tothe question which of which of the used function can describe the variation precisely. Alsoexponential function is analyzed as in the Botelho, Pinto (2004) research and results arecompared.Data on the number of Lithuanian mobile phone subscribers for the research is available forthe period 1996 – 2004, as 1996 was the first year when data about generation mobilecommunication was started to be collected in Lithuania. 43
    • Table 4.1 Cumulative data of mobile phone and fixed line subscribers. Mobile Fixed lineTime subscribers, subscribers,period thou. thou.1996 51 992,61997 150,8 1048,21998 267,6 1109,81999 343,6 1144,62000 508,9 1180,12001 1018 1144,52002 1631,6 929,62003 2152,6 827,82004 3421,54 776Source: Lithuanian Statistical Department, ( data and average income is adjusted is adjusted according to 2000 price level.Table 4.2. Economical variables AverageTime GDP, in income,period thou LTL LTL1996 38821 618,21997 41541 778,11998 44565 929,81999 43810 987,42000 45526 970,82001 48429 982,32002 51704 1013,92003 56716 1072,62004 60511 1157,8Source: Lithuanian Statistical Department, ( 44
    • 4.2.1 Correlation analysisIn Table 4.3 the results of strength of relationship between number of mobile phonesubscribers and yearly GDP is demonstrated. We can see that Pearson correlation coefficientimplies that positive correlation with very strong relationship exist between variable.Coefficient value 0,974 is very near to perfect correlations, meaning that the variables areprecisely related. Also we can observe that this correlation is statistically significant, withprobability value lower than 0,001.Table 4.3 Correlation. Number of mobile phone subscribers and GDP.Correlations Mobile_subscri GDP bers_thousMobile_subscribers_thous Pearson Correlation 1 0,974 Sig. (2-tailed) 0,000 N 9 9GDP Pearson Correlation 0,974 1 Sig. (2-tailed) 0,000 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 GDPIn Table 4.4 we can see the results of relationship between number of mobile phonesubscribers and average income. Pearson correlation coefficient value 0,773 demonstratesrather strong positive relations between variable. However we can clearly observe thatrelationship 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 Average_Incom bers_thous e Mobile_subscribers_thous Pearson Correlation 1 0,773 Sig. (2-tailed) 0,015 N 9 9 Average_Income Pearson Correlation 0,773 1 Sig. (2-tailed) 0,015 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 observablepattern 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 phonesubscribers and number of fixed phone line subscribers. We can see that Pearson correlationcoefficient implies that negative relationship exist between variables. Coefficient value -0,818is reasonably high, demonstrating those variables are strongly negatively precisely related.Also we can observe that this correlation is on the boundary of statistical significance, withprobability value 0,07.Table 4.5 Correlation. Number of mobile phone subscribers and fixed subscribersCorrelations Mobile_subscri Fixed_subscribe bers_thous rs_thousMobile_subscribers_thous Pearson Correlation 1 -0,818 Sig. (2-tailed) 0,007 N 9 9Fixed_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 variablesexist, but it is not expressed very explicitly.Figure 4.3. Scatter diagram for mobile phone subscribers and fixed line subscribers 4.2.2 Regression analysisIn the following simple and multiple regression models will be used to study the nature ofrelationship between number of mobile phone users and variables used in the correlationanalysis.Simple regression will be conducted between numbers of mobile phone users as dependentvariable and GDP, average income, number of fixed line subscribers. Also in the multipleregression independent variables will be paired in following manner: GDP and fixed linesubscribers; average income and fixed line subscribers 48
    • Conducting simple regression for number of mobile phone subscribers and number of fixedline subscribers we get results depicted in Table 4.4. Following regression equation can beconstructed:Mobile phone subscribers = 7.548 – 6,379 x Fixed line subscribersInterpretation of the obtained equation could be following: every unsubscribed fixed phoneline resulted in 6,379 mobile phone subscription in period 1995-2004. This seems ratherobvious – usually household in Lithuanians has one fixed line, but after unsubscription of thisline every household member purchases personal mobile phone.Table 4.6 Regression coefficients. Mobile phone subscribers and fixed line subscribers Unstandardized StandardizedModel Coefficients Coefficients t Sig. B Std. Error Beta (Constant) 7.548,067 1.740,016 4,338 0,0031 - Fixed_subscribers_thous -6,379 1,695 -0,818 0,007 3,762But we can see from the Table 4.6 that in the obtained regression equation variation ofindependent variable explains only 66.9% of variation of the dependent variable. It can besaid that this equation can not be seen as good predictor because about one third of variationremains unexplained. However Durbin-Watson very coefficient demonstrates that equationcan not be used for prediction.Table 4.7. Regression characteristics. Mobile phone subscribers and fixed linesubscribers Adjusted R Std. Error of Durbin-R R Square the Estimate Watson Square0,818 0,669 0,622 700,75193 0,657 49
    • Conducting simple regression for number of mobile phone subscribers as dependent variableand number of fixed line subscribers as independent variable we get results depicted in Table4.7. Following regression equation can be constructed:Mobile phone subscribers = -6.393 + 0,155x GDPThe 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 thismeasure can not be used as an objective predicting coefficient, because different countries dohave different GDP level and this does not clearly explain cause and effect.Table 4.8 Regression coefficients. Mobile phone subscribers and GDP Unstandardized Standardized Coefficients CoefficientsModel t Sig. Std. B Beta Error 663,6 (Constant) -6.393,482 -9,633 0,0001 90 GDP 0,155 0,014 0,974 11,341 0,000We can see in the Table 4.9 that in this regression model 94.8% of dependent variable isexplained by variation of dependent variable. This means that it is very precise model and canbe used for prediction. Durbin Watson coefficient is not significantly far from 2.Table 4.9. Regression characteristics. Mobile phone subscribers and GDP. Adjusted R Std. Error of Durbin-R R Square the Estimate Watson Square0,974 0,948 0,941 276,76174 1,527Following regression equation is designed to test the relation between mobile phonesubscribers as dependent variable and average income as independent: 50
    • Mobile phone subscribers = 4.144+5,505x Average IncomeInterpreting the equation we can say that every average income increases by 100 Litas ads 550thousand new mobile pone subscribers.Table 4.10 Regression coefficients. Mobile phone subscribers and average income Unstandardized StandardizedModel Coefficients Coefficients t Sig. B Std. Error Beta - - (Constant) 1.633,362 0,0391 4.144,789 2,538 Average_Income 5,505 1,706 0,773 3,227 0,015However this regression equation can be evaluated as rather misleading, because we can seefrom Table 4.11 that only 59,8% of independent variable variation is explained by dependentvariable variation. Also Durbin-Watson coefficient is far below the needed level of 2.Table 4.11 Regression characteristics. Mobile phone subscribers and average income. Adjusted R Std. Error of Durbin-R R Square the Estimate Watson Square0,773 0,598 0,541 772,35124 0,556Following regression equation is derived from the Mobile phone subscribers as dependentvariable and pair of fixed line subscribers and GDP as independent:Mobile phone subscribers = -3.448 - 1,69 x Fixed line subscribers + 0,13 x GDPWe can interpret that when 1 fixed line subscriber decides to discontinue the usage of thisservice, 1,69 additional mobile phone subscribers is observed. Also we can add that as GDPincreases by 1000 LT, additional 0.13 subscribers join the mobile communication network. 51
    • Table 4.12. Multiple regression coefficients. Mobile phone subscribers, fixed linesubscribers and GDP. Unstandardized Standardized Coefficients CoefficientsModel t Sig. Std. B Beta Error 1.534,1 - (Constant) -3.448,548 0,066 56 2,2481 - Fixed_subscribers_thous -1,690 0,822 -0,217 0,086 2,056 GDP 0,130 0,017 0,814 7,717 0,000R 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 linesubscribers and GDP. Adjusted R Std. Error of Durbin-R R Square the Estimate Watson Square0,985 0,970 0,960 228,98033 2,258Following regression equation (from Table 4.14) is derived from the mobile phonesubscribers as dependent variable and pair of fixed line subscribers and Average Income asindependent:Mobile phone subscribers = 2.271 - 4,861 x Fixed line subscribers + 3,947 x Average IncomeIn this equation we can say that when as every fixed line subscriber discontinues the usage ofthe service, 4.86 additional mobile phone subscribers join the network, and every 1 LT of theadditional average income brings 3.947 additional subscribers of mobile communicationnetworks. This is clearly overestimation despite the fact that R square and Durbin Watson 52
    • statistics demonstrate near perfect characteristics. Also constant coefficient significance is farabove the needed level. (Table 4.15)Table 4.14. Multiple regression coefficients. Mobile phone subscribers, fixed linesubscribers and average income. Unstandardized Standardized Model Coefficients Coefficients t Sig. B Std. Error Beta (Constant) 2.271,734 1.307,927 1,737 0,133 - 1 Fixed_subscribers_thous -4,861 0,842 -0,623 0,001 5,772 Average_Income 3,947 0,769 0,554 5,135 0,002Table 4.15 Multiple regression characteristics. Mobile phone subscribers, fixed linesubscribers and average income. Adjusted R Std. Error of Durbin- R R Square the Estimate Watson Square 0,969 0,939 0,918 325,88589 1,8264.3.3 Curve estimationIn the following section cumulative data of cellular phone subscribers (Table 4.1) is analyzedusing 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 ,001Logarithmic ,607 10,815 1 7 ,013Inverse ,353 3,824 1 7 ,091Quadratic 220,09 ,987 2 6 ,000 8Cubic 338,16 ,995 3 5 ,000 5Compound 303,01 ,977 1 7 ,000 9Power 160,37 ,958 1 7 ,000 4S ,785 25,527 1 7 ,001Growth 303,01 ,977 1 7 ,000 9Exponential 303,01 ,977 1 7 ,000 9Logistic 303,01 ,977 1 7 ,000 9We can clearly see that due to low R square value and significance higher than 0.05logarithmic and inverse function do have low predictability value and will be eliminated fromthe further research. For the models the significance value of the F statistic is less than 0.05for 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 andmodel-predicted values of the dependent variable. The large R Square values indicate strongrelationships for models other than logarithmic and inverse. Predictability of S function alsoremains 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 Mobile_subscribers_thous 4000,00 Observed Linear Quadratic Cubic Power 3000,00 2000,00 1000,00 0,00From the Figure 4.4 we can visually observe that only Cubic and Power function matches theobserved 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 Logisticsfunctions. Mobile_subscribers_thous 4000,00 Observed Compound S Growth Exponential 3000,00 Logistic 2000,00 1000,00 0,00We 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 havefollowing function for future consideration: Cubic, Power, Compound, Growth, Exponentialand 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 EstimatesEquation R Square F df1 df2 Sig. Constant b1 56
    • Exponenti ,977 303,019 1 7 ,000 46,828 ,492alAs 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 ofdetermination (0,977), for the estimated parameters.Model predicts that the number of cellular phone subscribers is growing at the rate of 64percent per year (13The coefficient estimate 0 ,492 shown in Table 4.3 is the estimatedinstantaneous 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 mobilephone subscribers for the period was 75% per year. Unlike the Botelho, Pinto (2004) we cansay that exponential growth can be successfully used for predicting the growth rate inLithuanian 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
    • ConclusionThe main idea of this master thesis is to examine the growth and development of the mobilecommunication industry by trying to find, analyze and evaluate the factors which had mostsignificant 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 mobilecommunications occur through a process of gradual evolutionary development and the„jumps“. No clearly observable continues pattern can be formulated because it is verydifficult to evaluate the impact of continues evolution and impact of occasional bounce.However currently we are witnessing development of only 3rd generation mobilecommunication, while in the past we had one development of analogue and one developmentof digital technology. It can be said, that after more generations of mobile communication willdeveloped throughout the years (discussion about 4th generation of mobile communication isalready happening) maybe it will be possible to find clearly observable pattern, characteristicsand objective parameters of evaluation.In the framework considered in Section 2 concepts of both network externalities and diffusionof innovations are interpreted in the light of current situation of mobile communication. Itappears that using network externalities reasoning mobile communication growth can besustainable explained. Especially this is true for the reasons of network externalities existence,findings about technology adoption in industries where network externalities, features ofcompeting systems, features of incompatible systems. Also analysis of the authors which tryto interpret and renew traditional network externalities concepts gives additional insights.As for the diffusion of innovation, Rogers (1963) segmentation of the potential adopters isvery useful and perceptive. This segmentation might be successfully applied in the marketresearches of the mobile communication services providers, as different groups of potentialadopters with different characteristics might be seen as target groups requiring specialtechniques of marketing. As diffusion of innovations concept is widely applied and developedin 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 theempirical 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 singlebest strategy for allocating scarce spectrum resource can be created. None of the discussedspectrum license allocation methods is perfect, but most of them do have specificconsiderations which might help to conduct the process efficiently. Of course beforeconsidering any of the methods of spectrum allocation, many factors must be taken intoaccount to assure the effective outcome. Also it can be said that original and insightfulapproaches to the problem might be extremely successful (case of Hong Kong). The mostimportant thing is that the chosen way of implementation of spectrum allocation should becarefully researched with possible consequences on the operators itself and future customers.The example of European 3G licensing demonstrates that short sighted thinking about initialgoverned revenues might even lead to very serious consequences for the development and theimplementation of the technology.Considering the Section 4 where empirical analysis of data was presented it can be said thatused statistical methods mostly confirm the theoretical presumptions about the factorsinfluencing growth of new technologies. As the distinction between economical andtechnological factors influencing mobile communication development was made, both factorsgroups demonstrated significant and observable impact. However due to relatively little dataavailable (some of the needed data are even uncollectible), not all the theoretical assumptionsmight be tested and explained.Finally following research ideas of Lithuanian mobile communication market could beconsidered 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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