Difference of Mobile Communication Technology: Assessing the Factors Affecting Cellular Mobile Telephone Diffusion at the Countr

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DIFFERENCES IN MOBILE COMMUNICATION TECHNOLOGY: ASSESSING THE FACTORS AFFECTING CELLULAR MOBILE TELEPHONE DIFFUSION AT THE COUNTRY LEVEL

YANG-HWAN LEE

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS IN MASS COMMUNICATION

UNIVERSITY OF FLORIDA

2005

Yang-Hwan Lee

To my parents, Yu Lee and Jung Hur; my wife, Kyung Nam Doh; and my daughter, Amy Sihyun Lee

ACKNOWLEDGMENTS

Foremost, I would like to thank Dr. Sylvia M. Chan-Olmsted, who provided me

with continuous support and guidance throughout my graduate career. Without her genuine support, this thesis could not have come to realization. She offered great academic insight and emotional support to me. I also owe much to Dr. Leonard Tipton and Dr. Justin Brown, who were always willing to help. They provided guidelines, encouragement, and knowledge in their area of expertise.

I would like to thank all of my Korean friends in the College of Journalism and

Communications. They provided consistent support and friendship throughout my

master’s program. I also benefited enormously from Dr. Chang-Hoan Cho and Dr. Yoo-

Jin Choi. They always helped me either academically and emotionally.

My deepest appreciation goes to my parents, Yu Lee and Jung Hur, for their

emotional and financial support. Their support enabled me to overcome all difficulties. I

am also grateful for the patience and dedicated love of my wife, Kyung Nam Doh. She always believed in me and devoted herself to supporting me. Her heartfelt wish helped me greatly to finish the master’s program successfully. Thanks to them, my life has been full of joy. I shall never forget this memory with them and I wish their every happiness.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... iv

LIST OF TABLES...... viii

LIST OF FIGURES ...... x

ABSTRACT...... xi

CHAPTER

1 INTRODUCTION ...... 1

2 LITERATURE REVIEW AND THEORETICAL FRAMEWORK...... 5

The Development of Cellular Mobile Phone Technology...... 5 The Beginnings and Classification of Cellular Mobile Phone Technology...... 5 The Pre-Cellular Mobile Era ...... 9 From Pre-Cellular Mobile to 1G ...... 9 Digital Cellular Mobile: 2G - 2.5G ...... 11 Multimedia Cellular Mobile: 3G...... 15 Broadband Cellular Mobile: 4G ...... 19 The Global Cellular Mobile Phone Market ...... 21 Various Cellular Mobile Phone Market in the World ...... 25 Europe, the Middle East and the African region...... 26 The Asia Pacific region...... 28 The Americas ...... 30 Digital Divide between Developing and Developed Countries...... 31 Definitions of Developed and Developing Countries ...... 31 Digital Divide and Leapfrogging...... 32 Theoretical Background...... 36 Factors Affecting the Diffusion of Cellular Mobile Phone Technology ...... 40 Individual, Firm, and Country-Level Adoption and Diffusion ...... 40 Technology Infrastructure Factors ...... 42 Regulatory and Policy Factors...... 44 Cultural Factors ...... 44 Demographic Factors...... 45 Other Integrated Studies...... 46 Proposed Variables and Hypotheses...... 47

v Economic Development Factors...... 47 Economic Freedom...... 49 International Business Trade Openness...... 49 Technological Infrastructure Factors...... 50 Consumer-Related Factors ...... 52 Political Environment...... 53 Cultural Factors ...... 55 Market Size...... 56 Research Questions...... 57

3 METHODOLOGY ...... 60

Research Method ...... 60 Sample Selection and Data Sources ...... 61 Sample Selection ...... 61 Data Source ...... 62 Variable Operationalization and Measurement ...... 62 Dependent Variable...... 62 Independent Variables and Measurement ...... 63 Economic development ...... 63 Technology infrastructure variables...... 64 Economic freedom ...... 64 International business/trade openness ...... 65 Consumer-related variables...... 65 Political environment ...... 65 Cultural-related variables ...... 66 Digital divide measurement ...... 67 Statistical Procedures...... 69

4 RESULTS...... 72

Descriptive Statistics ...... 72 Overall Cellular Mobile Phone Penetration in 103 Countries...... 72 Review of the Suggested Independent Variables ...... 76 Correlations between Variables and Multicollinearity...... 79 Correlations between Variables...... 79 Multicollinearity Problems...... 81 Hypotheses Tests ...... 82 The Two-Way Random Effect Panel Regression...... 82 Hypotheses Tests...... 83 Research Questions...... 85 Panel Regression versus OLS Regression...... 85 Major Factors from 1996 to 2002...... 89 Identifying Digital Divide in Cellular Mobile Phone Diffusion ...... 91 Descriptive Statistics Review...... 91 Empirical Test ...... 98

vi 5 DISCUSSION AND CONCLUSION...... 100

Study Overview ...... 100 Conclusions and Suggestions ...... 101 Descriptive Statistics and Correlations...... 101 Hypotheses Testing and Research Questions...... 104 Economic-Related Variables...... 105 Technology Infrastructure Variables...... 106 Other Variables ...... 108 Major Factors Affecting Mobile Diffusion by Year ...... 112 Digital Divide and Mobile Diffusion ...... 113 Contributions ...... 114 Limitation and Suggestions for Future Research ...... 115

LIST OF REFERENCES...... 117

BIOGRAPHICAL SKETCH ...... 128

vii

LIST OF TABLES

Table page

2-1 Mobile Technology from Pre-Cellular Mobile to 4G Era...... 7

2-2 IMT-2000 System/Capability...... 15

2-3 Increase in ICT Diffusion in the 7 Most Populous Countries...... 57

3-1 List of Variables and Data Sources (1996-2002)...... 66

3-2 Developed and Developing Countries in Selected Samples ...... 68

4-1 Top and Low Ranked Countries in Cellular Mobile Subscribers per 100 Inhabitants ...... 74

4-2 Comparison of Cellular Mobile Phone Subscriber Growth in 103 Countries...... 75

4-3 Descriptive Statistics of Independent Variables...... 78

4-4 Correlation Matrix...... 80

4-5 Excluded Variable by SPSS ...... 82

4-6 The Result of the Two-Way Random Effect Panel Regression ...... 82

4-7 Summary of Hypotheses Testing (considered cross section and time-series)...... 85

4-8 Result of the OLS Regression (not considered cross section and time-series) ...... 86

4-9 Panel regression versus OLS regression ...... 87

4-10 Results of OLS Regressions by Years (1996-2002)...... 89

4-11 Cellular Mobile Phone Subscribers* in Developed Countries (1996-2002)...... 91

4-12 Cellular Mobile Phone Subscribers* in Developing Countries (1996-2002) ...... 93

4-13 Cellular Mobile Phone Subscriber Growth Rates* in Developed Countries ...... 94

4-14 Cellular Mobile Phone Subscriber Growth Rates* in Developing Countries...... 96

viii 4-15 Average Growth Rates in Each Developed Country (1996-2002)...... 97

4-16 Average Growth Rates in Each Developing Country (1996-2002) ...... 98

4-17 Descriptive Statistics of Independent Sample T-test...... 99

4-18 The Result of Independent Sample T-test ...... 99

LIST OF FIGURES

Figure page

2-1 Transition of Technology Standards from 1G to 3G...... 14

2-2 3G/UMTS Customers Growth as of 2004...... 18

2-3 Ownership of Internet Mobile Phone 2003 ...... 20

2-4 Numbers of Subscribers 2001-2004 (CDMA-2000/W-CDMA)...... 22

2-6 Global Internet Browsing Users 1999-2005...... 25

2-7 Theoretical Framework ...... 59

4-1 Cellular Mobile Phone Subscribers in the World (1996-2002)...... 73

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Arts in Mass Communication

DIFFERENCES IN MOBILE COMMUNICATION TECHNOLOGY: ASSESSING THE FACTORS AFFECTING CELLULAR MOBILE TELEPHONE DIFFUSION AT THE COUNTRY LEVEL

Yang-Hwan Lee

December 2005

Chair: Sylvia M. Chan-Olmsted Major Department: Journalism and Communications

My study reviewed the recent development of cellular mobile phone technology and proposed a comprehensive framework for cellular mobile phone diffusion in different countries, by integrating a number of different factors that might affect cellular mobile phone diffusion. My study also investigated differences in cellular mobile phone diffusion for developed versus developing countries.

I selected and analyzed the 103 countries’ cellular mobile phone penetration rates from 1996 to 2002. Using the panel regression, I examined the effect of independent variables on cellular mobile phone penetration rates while controlling for country and time. Comparing panel regression and OLS regression, I showed whether the time factor influences the mobile phone diffusion. I also examined what kind of independent variable is significant in each of the given years, using additional OLS regressions. Finally, I analyzed the 103 countries’ subscriber data and mobile penetration growth rates

xi determine if any digital divide exist in cellular mobile phone diffusion in developed

versus developing countries.

In general, controlling for country and time, only economic factors and technology infrastructure factors were significant in the panel regression. However, the variance

component for cross sections was higher than the variance component for time-series.

This implies that the time effect to the mobile diffusion was minimal.

When the time factor was not controlled, the factors affecting cellular mobile phone

diffusion were quite different from the results of the panel regression. Some independent

factors were significant only under controlling for country and time factors, others were only significant when country and time factors were not controlled. When I performed the OLS regression separately by year, only GDP per capita, DAI, Internet user penetration, and individualism supported mobile diffusion.

Overall, although the mobile growth rates in the developing countries exceeded those rates of the developed countries during recent year, the gap of mobile phone penetration between the developed and developing countries examined have actually widened significantly. This result was also supported by empirical test.

xii CHAPTER 1 INTRODUCTION

The spread of cellular mobile communication throughout the world has been

remarkably rapid during the past decade. Deutsche Bank (2004) estimated that global

subscribers exceeded 1.5 billion in 2004 and will reach 2.3 billion by 2010. According to

The Global Information Technology Report (World Economic Forum, 2004), 1 in every 5 people are cellular mobile phone subscribers, out of the 6.2 billion world population in

2002; an increase from 1 in every 12 people three years ago. From 1999 to 2002, the number of global cellular mobile phone subscribers grew 134% and exceeded the number of main telephone lines in operation. The largest increases in the number of cellular mobile subscribers occurred in the most heavily populated countries. For example, the highest increase in the number of cellular mobile phone subscribers was in China where they posted 163 million mobile subscribers; the United States posted 55 million subscribers. The world Economic Forum also indicates that cellular mobile phone subscribers now exceed the number of main telephone lines in many middle- and low-income countries (such as Mexico and Indonesia), as well as high-income countries.

Based on the growth in cellular mobile phone subscribers, mobile commerce

(m-commerce) has emerged as a new electronic commerce market. The rapid growth of mobile technology has fueled the expansion of the mobile Internet as a foundation for m-commerce. M-commerce refers to e-commerce via mobile phone (Mennecke &

Strader, 2002). Because mobile Internet users can connect to the Internet wherever, whenever, and mobile has some advantages over stationary Internet technologies. There

1 2 are wide-range forecasts for the m-commerce market worldwide. Ovum consulting estimated that the worldwide value of m-commerce will reach $210.8 billion by 2005 (M- commerce, n.d.).

Although the technology of mobile communication has been available since the early 1960s, the major drive for the pervasive adoption of mobile services is due to the introduction of more advanced technology (Rappaport, 2002; Garg & Wilkes, 1996;

Gruber & Verboven, 2001). Digital technology, especially, has played an important role in changing the cellular mobile phone industry environment. Digital mobile phone technology led to changes from first-generation (1G, analog) to second-generation (2G, digital), and, finally, the third-generation (3G) wireless technology standard, IMT-2000

(International Mobile Communications 2000 Initiative), issued by ITU in 1999 (Auter &

Adams, 2004). The 3G standards permit greater bandwidths, and also embrace multimedia transmission, integrating voice, data, and two-way video transmission

(Wareham & Levy, 2002). In the middle of the 3G transition, researchers for the leading mobile players were already laying the groundwork for what some called 4G and others termed the undefined “wireless world,” which is expected to become operational between

2008 and 2011 (Steinbock, 2003). Continuous technological improvements extend the cellular mobile phone market and increase the potential universe of cellular mobile phone subscribers with the conjunction of the mobile telephone and the public switched telephone network (PSTN) (Auter & Adams, 2004).

In addition to technological advances, other factors have played a role in cellular mobile phone diffusion. Researchers have considered governmental policy and regulation to be important components in cellular mobile phone diffusion (Rappaport, 2002; Garg

and Wilkes, 1996; Gruber and Verboven, 2001). Studies have shown that a government’s

policies and regulatory factors influence broadband Internet diffusion. For example, in

Korea, policy factors play the most significant role in the diffusion of broadband Internet

(Ryu, Kim, & Kim, 2003). Because the Korean government has consistently pursued a

telecommunications policy that encourages competition and new service development, its

telecom market has been able to reach a high level of broadband Internet adoption.

Research has concluded that technology standards, timing, and the number of mobile

phones license could be affected by the government’s decisions toward that industry

(Gruber and Verboven, 2001).

Empirical studies have also investigated the diffusion of mobile technology at the

country level. In fact, many scholars have examined the factors that drive the growth of

Information and Communication Technology (ICT) diffusion in different countries. For

the most part, these studies tend to center around one or two specific variables. In the

same way as other ICT research, mobile diffusion research has been approached using

existing variables related to economic and regulatory aspects. Only a few studies have

adopted integrated perspectives with additional independent variables. The aim of this

study is to provide an overview of the recent development of cellular mobile phone

technology, and to propose a comprehensive framework for mobile diffusion in different

countries by integrating a number of different factors that might affect cellular mobile

phone diffusion. Furthermore, this study will empirically test this comprehensive list of factors.

In addition, I will also investigate the differences between mobile diffusion in

developed countries and mobile diffusion in developing countries. The global economy

4 has been driven by a greater integration of the world’s markets and a drastic growth of

ICT, however, data by countries show a global digital divide (Baliamoune-Lutz, 2003).

Most technology diffusion studies have dealt with developed countries equipped with a well-developed infrastructure. This study will identify whether or not a gap in mobile diffusion exists between developed and developing countries and, if so, how the pattern of mobile diffusion differs between these two types of countries.

CHAPTER 2 LITERATURE REVIEW AND THEORETICAL FRAMEWORK

In this study, I reviewed earlier studies and research frameworks associated with

technology diffusion. Specifically, I summarized the process of cellular mobile phone

technology and market development. Then, I reviewed relevant studies and the theoretical background of technology diffusion. Researchers have suggested that a variety of factors are associated with the diffusion of mobile phone technology in any country.

To investigate the differences in the diffusion of mobile phone technology among various countries, I will review these factors and propose an integrated framework. Finally, a specific research hypothesis will be proposed.

The Development of Cellular Mobile Phone Technology

In this section, I reviewed several forms of technology and their development phases throughout time. Beginning with the pre-cellular mobile era, mobile technologies have been developed through the first generation (1G), second generation (2G), third generation (3G), and finally, fourth generation (4G). Each generation of mobile communication has been based on a dominant technology, and some countries have played a great role in the evolution of mobile technology.

The Beginnings and Classification of Cellular Mobile Phone Technology

Fundamentally, a cellular mobile phone is a kind of two-way radio. Therefore, the history of the cellular mobile phone begins with the history of wireless transition including radio transmission. But before radio transmission evolution, there were several pioneers who built the foundation of wireless technology. After the invention of the wire

5 6

telephone in 1876, Alexander Graham Bell succeeded in sending a message by telephone

from one ship to another over the distance of a mile. A German scientist, Heinrich Hertz,

discovered “Hertzian” waves in 1888 and established the basic concept of spark

transmission, which was used in radio equipment until 1915 and remained widely used in

other devices until the 1930s (Steinbock, 2003). Generally, Guglielmo Marconi, an

Italian inventor, has been known for initiating mobile technology innovations. He proved that electric waves could be transmitted at a significant distance and developed the first

practical business models for wireless telecommunication (Steinbock, 2003).

Meanwhile, the term “cellular” began to emerge in the 1940s. Cellular is the type of

wireless communication that is most familiar to mobile phone users. It is ‘cellular’

because the system uses many base stations to divide a service area into multiple cells

(Bellis, n.d.). Cellular calls are transferred from one base station to another as a user

travels from cell to cell. This new concept that could improve the coverage of existing

mobile phones began in 1947 (Auter & Adams, 2004), and, since the early 1980s,

wireless technology has developed rapidly through successive cellular technology

platforms that have given rise to analog (first generation, 1G), digital (2G, 2.5G),

multimedia cellular (3G), and is currently leading to broadband cellular (4G). Each era

coincides with different, yet fundamental, wireless technologies: frequency division

multiple access (FDMA), time division multiple access (TDMA), and code division

multiple access (CDMA) (Steinbock, 2003).

In FDMA, the total system bandwidth is divided into frequency channels that are

allocated to users. From the invention of the radio to the rise of 2G, this was the most

common analog system (Steinbock, 2003). TDMA allows each user to access the entire

radio frequency channel for the short period of a cell. Other users share this same

frequency channel at different time slots (ITU, 2003a). TDMA has dominated the 2G

digital cellular mobile era. CDMA is a digital mobile technology that allows multiple

users to share radio frequencies at the same time (Third Generation Mobile Technology, n.d.). Transmissions are spread over the whole radio band, and each voice or data call is assigned a unique code to differentiate itself from the other calls carried over the same spectrum (ITU, 2003a). New 3G services are almost based on CDMA technologies. 2G services have used the original CDMA IS-95, but 3G services will use new high-speed versions of CDMA, either Wideband CDMA (W-CDMA) or its competing technology,

CDMA 2000.

In a nutshell, the history of mobile technology can be divided into 6 distinct generations (the pre-cellular mobile era, 1G, 2G, 2.5G, 3G, and 4G), and each generation of mobile communication has been based on a dominant technology that has significantly improved spectrum capacity (Table 2-1). The next section discusses each generation and focuses more closely on technology development.

Table 2-1. Mobile Technology from Pre-Cellular Mobile to 4G Era Mobile Dominant Characteristics Technology Generations MTS Pre-Cellular - Narrowband FM channel IMTS Mobile - Automatic trunking - Direct dialing - Full-duplex service FDMA 1G - Only one user is assigned to a channel at a time (AMPS, (Analog - Analog voice service NMT, and Cellular - No data capabilities TACS) Mobile) - Three different versions: AMPS in the U.S., NMT in Nordic countries, TACS in the U.K. - Compared to other multiple access schemes, FDMA is the most inefficient

Table 2-1 Continued Mobile Dominant Characteristics Technology Generations TDMA 2G - Up to 10 kbits/s (GSM) (Digital -Each frequency channel is divided into time slots and CDMA IS-95 Cellular each user is allocated a time slot which improves Mobile) spectrum capacity - Digital voice service - Advanced messaging (email, digital text delivery, and short message services (SMS)) - Global roaming - Circuit-switched data - Three different versions of TDMA: North America TDMA, European TDMA (GSM), Japanese TDMA (PDC/PHS) GPRS 2.5G - Up to 384 kbits/s HSCSD (Digital - Extension of GSM; HSCSD, GPRS (packet-switched EDGE Cellular data), EDGE (faster than GPRS) CDMA- Mobile) - Extension of CDMA; CDMA-1XRTT, HDR 1XRTT - New applications: mobile banking, voicemail through HDR the Web, mobile audio player, digital newspaper publishing, digital audio delivery, mobile radio-karaoke, mobile coupons IMT-2000 3G - Dominant interface: IMT-2000 UMTS (Multimedia - Support of multimedia services/capabilities; fixed and (CDMA 2000 Cellular variable rate bit traffic, bandwidth on demand, WCDMA) Mobile) asymmetric data rates in the forward and reverse links, multimedia mail store and forward, and broadband access up to 2 Mb/s - IP enabled - Global roaming - New applications: video phone/mail, mobile TV/video player, advanced car navigation, remote medical diagnosis and education HSDPA 4G - Up to 20~100 Mbits/s MO-WLAN (Broadband - Future transition to broadband cellular Cellular - No change in interface technology Mobile) - Seamless convergence; the users of mobile devices would roam freely from one standard to another- pervasive computing - 4G is expected to become operational between 2008 and 2011 Source: ITU (2003, 2001a, 2001b); Auter & Adams, 2004; Steinbock (2003); FCC (2000)

The Pre-Cellular Mobile Era

If Marconi’s device dominated the first two decades of the twentieth century, a distinction can be made between the ensuing pioneer phase (1921-1945), which was dominated by conventional amplitude modulation (AM) techniques, and the commercial phase (1946-1968), which was driven by frequency modulation (FM) techniques

(Calhoun, 1988). Compared to AM radio, the range of FM was tripled and needed much less power, which paved the way for vehicular transmitters and sensitive receivers.

During World War II, two-way FM transmission proved easier to use and jammed less than two-way AM (Auter & Adams, 2004). After the war, AT&T introduced the first commercial wireless service, Mobile Telephone Service (MTS), including the narrowband FM channel in 1946, and they proposed the allocation of a large number of radio-spectrum frequencies because the system was limited by a lack of frequencies

(Steinbock, 2003; Auter & Adams, 2004). However, the Federal Communications

Commission (FCC) decided to limit the amount of frequencies and allowed only 23 simultaneous phone conversations in any given area. FCC reconsidered its decision in

1968 (Bellis, n.d.). Before the advent of analog transmission for voice communication,

MTS and IMTS (Improved Mobile Telephone Service) served as the basic technology of the pre-cellular era (Steinbock, 2003).

From Pre-Cellular Mobile to 1G

Throughout the pre-cellular mobile era, the mobile industry was haunted by the dilemma of how to provide service to as many customers as possible, while still using a limited amount of resources. This problem was solved in the late 1960s with the development of the first analog systems. Several systems emerged worldwide, but only 3 proved enduring: Advanced Mobile Phone Service (AMPS), Nordic Mobile Telecom

(NMT), and Total Access Communication System (TACS). In the early 1980s, the initial stage of analog systems operated in a portion of the spectrum around the 450 MHz frequency band; but during the late 1980s, analog systems operated in a portion of the spectrum around the 900 MHz frequency band (Gruber & Verboven, 1999). Initially, the market leader was AT&T in the US who introduced the first commercial land mobile radio telephone system in St. Louis in 1946. The concept of cellular was introduced into the AMPS architecture in 1983. Within the US, operators adopted the common AMPS standard and relied on FDMA technology in the 800 MHz to 900 MHz frequency bands.

More recently the 1800 to 2000 MHz band has been embraced (Steinbock, 2003).

In the pre-cellular mobile era, from the 1920s to the early 1980s, the US led the world in mobile technology standards. The US telephone industry invented cellular telephony and maintained a strong lead in the march to deployment through the late

1970s. But, in the advent of 1G, Nordic countries and Japan caught up in terms of technological capabilities. The US was not the first country to launch commercial cellular mobile phone services. Japan’s Nippon Telephone and Telegraph launched services in

1979. NMT of the Nordic countries was launched in 1981. The Nordic countries’ and

Japan’s telecom authorities had few doubts about the need for their cellular system and its feasibility. However, in the US, public policy implementers perceived of the competitive environment very differently. In the end, this resulted in the acceleration of wireless activities and the first test in the US in 1978. Initially, US researchers had come up with the cellular concept in 1947 at Bell Labs, but it was commercialized by AT&T in 1983.

Regulation issues and failures explain the delays. In contrast, in Europe, government regulations and investments in technology were emphasized as key drivers in the speedy

diffusion of mobile phone communications services (Gruber & Verboven, 2001). In the

case of European analog mobile systems, NMT was supported by Finland’s Ministry of

Trade and Industry.

As mentioned earlier, Nordic countries launched an intrinsic analog mobile system.

NMT (in 1981) and the European marketers pushed European countries to adopt NMT.

However, because different standards were introduced in each European country, such as

TACS in the UK, and a large variety of proprietary systems in France, Germany, Italy and Japan, their efforts were fruitless. Today, AMPS is the most popular analog system and the second-largest cellular system worldwide after GSM (ITU, 2003a). The proprietary systems developed by the European players fragmented the market.

Digital Cellular Mobile: 2G - 2.5G

The transition from analog to digital technology was a noteworthy aspect in the time change from 1G to 2G. The diffusion of 2G began in January 1992 when the first wireless digital telecommunications network started operating in Finland (Koski &

Kretschmer, 2005). Analog cellular mobile phones never reached high levels of penetration for several reasons such as, technological uncertainty, inefficiency in spectrum use, and a lack of competition. The limitation in the number of channels available is the greatest shortcoming of analog cellular (Bellis, n.d.). In contrast, digital technology can provide a drastic increase in the efficiency of spectrum use and in the quality of service. Initially, digital technology was introduced in the same 900MHz spectrum as analog technology, but because of its more efficient use of the spectrum, digital technology can provide more increased capacity, accommodating three to four times more customers (Steinbock, 2003; Gruber & Verboven, 1999).

The most widely used 2G digital system is the European standard, the Global

System for Mobile Communications (GSM). GSM, which was formed in the late 1980s by 13 European countries, was developed by the Conference of European Posts and

Telecommunications (CEPT). They specified the necessary technical standards for the development of a pan-European digital cellular mobile communication system (Massini,

2004; Bellis, n.d.; Steinbock, 2003). GSM is based on an improved version of TDMA technology, and it operated in the 900, 1800, and 1900 MHz frequency bands (Auter &

Adams, 2004; Steinbock, 2003; Gruber & Verboven, 1999). Around 1990-1991, GSM dominated more than 99.3% of the earliest digital cellular markets, whereas the US’s

TDMA had only 0.7% (Steinbock, 2003). The GSM standard has been accepted in the

US since 1995 (Bellis, n.d.).

GSM 1900 cellular systems have been operating in the US since 1996. AT&T

Wireless, Omnipoint, Pacific Bell, BellSouth, Sprint Spectrum, Microcell, Western

Wireless, Powertel and Aerial also use GSM (Auter & Adams, 2004; Bellis; n.d.). Today,

GSM is the most dominant cellular standard in the world (ITU, 2003a). Deutsche Bank

(2004) estimates that global mobile subscribers will reach 2.3 billion by 2010, and they expect that at least 85% of the world’s next generation wireless customers will utilize

GSM technologies for both voice and data services.

Since the late 1990s, the primary digital technology in the US has been CDMA, although, GSM is popular worldwide. CDMA is used by 71 million people in the US, while GSM is used by 22 million people in the US (Auter & Adams, 2004). CDMA was created and commercially developed by Qualcomm in the late 1980s and it became one of the world’s fastest-growing wireless technologies (Bellis, n.d.). Asian-Pacific

countries and the US were the leaders in accepting CDMA standards with South Korea

holding the largest number of CDMA subscribers worldwide, almost 60% of the market

(ITU, 2003a; Steinbock, 2003). In 1999, as a major turning point for CDMA, the

International Telecommunication Union (ITU) selected it as the industry standard for the new 3G wireless system (Steinbock, 2003).

Between 2G and 3G is an interim step referred to as 2.5G. The evolution of network technology from 2G to 2.5G enabled users to send and receive data over a wireless platform (ITU, 2001a). Like 2G, 2.5G services also use digital, but they employ packet switched data transmission (Auter & Adams, 2004). Depending on the existing network, there are two different routes a cellular carrier can take to migrate from 2G to

2.5G (Figure 2-1). For GSM providers, a logical extension to 2.5G would be either High

Speed Circuit Switched Data (HSCSD) or General Pack Radio Service (GPRS), and

Enhanced Data Rate for GSM Evolution (EDGE). For CDMA providers, the route is only

CDMA-IXRTT or High Data Rate (HDR).

GRPS is a packet-switched technology. The main benefit of GPRS is that it can be

provided on the basis of an ‘always on’ permanent connection to the Internet, thereby

avoiding dialup delays. HSCSD is another GSM technology introduced in 1999 to enable

GSM technologies to increase data speeds. The most significant advantage of HSCSD is

that, because it utilizes the existing network and requires only a software upgrade, it can

reduce costs. EDGE is regarded as a cost-efficient way of migration to full-blown 3G

services. The capacity and efficiency of EDGE are improved by using a more advanced

coding scheme. This technology is also regarded as the final network-upgrade stage

technology that operators will seek to deploy before the launch of full broadband

14 services. However, EDGE is retained in the development stage and there is no technical standard agreement in Europe.

CDMA-1XRTT can carry out similar functions as GPRS. Most CDMA operators currently run their networks with CDMA IS-95, and CDMA-1XRTT is the next-phase of

CDMA IS-95 technology that can deliver transmission speeds up to 144Kbit/s. CDMA systems can use a data-only 2.5G standard called HDR. HDR is optimized for packet service with flexible architecture based on IP protocol. It has been referred to as

Qualcomm’s answer to EDGE (ITU, 2001a).

CDMA-based systems and TDMA counterparts have a very different way to IMT-

2000. CDMA 2000 systems are equivalent to 3G, while TDMA systems work with the

Ericsson-proposed W-CDMA standard to achieve 3G technology (ITU, 2001b).

1G 2G 2.5G 3G

CDMA CDMA-IXRTT IMT-2000 IS95 HDR CDMA 2000

AMPS GPRS EDGE GSM (TDMA WCDMA based)

1980s 1990s 2000-2005 2005-2010 Source: Auter & Adams (2004); ITU (2001a; 2001b)

Figure 2-1. Transition of Technology Standards from 1G to 3G

The 2G and 2.5G eras are indebted to technological advancement. Based on digital technology, GSM (CDMA in the US) as a unified system created the economies of scale, and have provided affordable mobile communications services to all. It is doubtful whether mobile communications would be where they are today without the advent of the

GSM standard. Its ready acceptance as the de facto global standard has been a key driver

15 for mobile phone growth in many regions throughout the world (Deutsche Bank

Research, 2004).

Multimedia Cellular Mobile: 3G

In the mid-1980s, the ITU created the global standard called “International Mobile

Telecommunications – 2000” (IMT-2000) to serve as the foundation of the 3G system for mobile communication (Steinbock, 2003; ITU, 2001b). Table 2-2 describes the most important capabilities of the 3G system (FCC, 2000).

Table 2-2. IMT-2000 System/Capability Capabilities to support circuit and packet data at high bit rates: - 144 Kbit/s or higher in high mobility (vehicular) traffic - 384 Kbit/s or higher for pedestrian traffic - 2Mbit/s or higher for indoor traffic

Interoperability and roaming among the IMT-2000 family of systems Common billing/user profiles: - Sharing of usage/rate information between services providers - Standardized call detail recording - Standardized user profiles

Capability to determine geographic position of mobiles and report it to both the network and the mobile terminal

Support of multimedia services/capabilities: - Fixed and variable rate bit traffic - Bandwidth on demand - Asymmetric data rates in the forward and reverse links - Multimedia mail store and forward - Broadband access up to 2 Mbit/s Source: FCC (2000)

Essentially, 3G concepts aim to ensure global interoperability and standardized usage of spectrum frequency. Implementing 3G is really about service ubiquity, and the freedom and convenience of conducting business from anywhere, at any time, enabling the valuable extension of global person-to-person communications. This service is always

16 on, mobile centric, and accessible across countries to deliver the expectations of the

Internet. Based on this, IMT-2000 offers the capability of providing voice and data services and applications. One of the key aspects of IMT-2000 is the ability for users to have personal terminals to go anywhere in the world, have access to a minimum set of voice telephony, and enjoy a selection of data services (ITU, 2003a; FCC, 2000). IMT-

2000 also provides services to fixed users and when a rapid and economical implementation of fixed communications is required. Because IMT-2000 has higher transmission rates (minimum speeds of 2Mbit/s) than 2G technologies, it is of particular interest to developing countries (FCC, 2000).

When the ITU tried to unify and standardize 3G technologies, no consensus was reached. There were thus 5 standards developed as part of the IMT-2000 terrestrial radio interface: IMT-DS, IMT-MC, IMT-TC, IMT-SC, and IMT-FT (ITU, 2003a). The two main interfaces fall under the European- and Japanese-supported W-CDMA and the US- and Korea-supported CDMA 2000 (ITU, 2001a). They are somewhat similar, but different enough that W-CDMA handsets will not work with CDMA 2000 handsets and visa-versa.

CDMA 2000 technology evolved from CDMA IS-95. According to the CDMA

Development Group (CDG, 2004a), CDMA 2000 offers a viable solution for universal access with a range of services, coverage advantages, equipment availability and cost efficiency. First, CDMA 2000 supports not only voice and broadband Internet access for individuals, businesses, schools and hospitals, but also a number of applications for public safety and communication markets. Secondly, CDMA is a highly efficient technology that transmits large amounts of voice and broadband traffic in a small

spectrum; only CDMA 2000 can operate in as little as 5 MHz of spectrum. Thirdly, it

provides service in widely populated areas in a cost-effective way; CDMA 2000 can cover the most geography with the least amount of infrastructure. Fourthly, by using

CDMA 2000, operators can create a competitive cost structure. For example, with 108 commercial networks and 37 more scheduled for launch by the end of 2004, CDMA 2000 offered significant economies of scale. Finally, the large amount of CDMA devices can be provided to people who want and need it, and CDMA 2000 phone prices will be expected to decline from $77 in 2004 to $44 in 2007 (Shosteck Group Forecast, 2004).

As of September 2004, more than 127 million people worldwide were using CDMA

2000. Furthermore, 59 vendors supplied over 680 models of phones and other devices, including 300 models for the 800 MHz bands and 24 for the 450 MHz bands (CDG,

2004a). CDMA 2000 will continue to expand and capture a larger market share. The

Yankee Group has forecasted that CDMA 2000 will reach over 369 million users by the end of 2008 (CDG, 2004b).

Regardless of location or socio-economic condition, providing everyone with reasonably priced access to mobile and broadband service is a top priority for many governments and a new opportunity for wireless operators. The most important issue for governments and wireless operators is selecting a technology that can deliver voice and data cost-effectively. In this respect, CDMA 2000 has shown that it delivers voice and broadband access economically and reliably, which makes it an optimal solution for worldwide services (CDG, 2004a).

W-CDMA is the competitor to CDMA 2000 and one of two 3G standards that makes use of a wider spectrum than CDMA and, therefore, can transmit and receive

18 information faster and more efficiently. In Europe, these high-speed systems came to be called Universal Mobile Telecommunications Systems (UMTS). European UMTS includes W-CDMA technologies with a core network specification based on GSM standards (Third Generation Mobile Technology, n.d.). UMTS is intended to provide advanced data speeds and protocols to allow people more reasonable access to the

Internet, to watch movies, exchange large data files, and have video conference calls to and from temporary locations based on choice and convenience (ITU, 2001b). By the end of 2004, there were more than 16 million 3G/UMTS customers subscribing to 60 networks based on W-CDMA technology in 25 countries (UMTS Forum, 2005).

10 9 7.2 8 6.7 Asia-Pacific 6 5.1 3.4 4 Europe 4 ` 2.1 2.2 Rest of the World 2 1 0.6 millions of customers of millions 0.1 0.3 0 Dec-03 Mar-04 Jun-04 Sept-04 Dec-04

Source: UMTS Forum (2005)

Figure 2-2. 3G/UMTS Customers Growth as of 2004

In Japan, another W-CDMA-centered country, Freedom of Mobile Multimedia

Access (FOMA) began at the end of May 2001 (Third Generation Mobile Technology, n.d.). Supported by European mobile operators, FOMA developed from NTT-DoCoMo and is expected to compete with CDMA 2000 to be the 3G standard.

It is likely that a 3G mobile wireless world will unite mobility and the Internet, but a certain degree of technological development in a society is a prerequisite for the union.

For instance, new mobile-centric applications would have to be developed before relying

on PC-centric development. Because the new 3G technology will revolutionize old

business models, new business models are also needed. Furthermore, the success of 3G depends on the ability of all interested parties to work together. In other words, the complexity of mobile communication means that it is no longer the domain of government regulatory bodies to determine how, when, and for how much service will be offered (Deutsche Bank 2004).

Broadband Cellular Mobile: 4G

As mentioned earlier, the major step from 2G to 3G and later generation mobile telecommunications was the ability to support advanced, wideband multimedia services, including email, file transfers, and distribution services like radio, TV, and software

provisioning. In the next generation of mobile communication, 4G, the combination of

the world’s different Information Technology (IT) industries, the media industry, and telecommunications will integrate communication with IT (Lu, 2003). As a result, mobile

telecommunications together with IT will penetrate the various fields of the world. The

ultimate objective of 4G is a seamless union in which users of mobile devices would

roam freely from one standard to another –pervasive and ubiquitous computing

(Steinbock, 2003).

In future 4G mobile telecommunications, two economically contradictive demands

arise: ubiquity and diversity (Lu, 2003). Ubiquitous communications free people from

spatial and temporal constraints. Versatile communication systems create customized

services based on diverse individual needs. The flexibility of mobile IT can satisfy these

demands simultaneously. Therefore, mobile IT plays a key role in the 21st century.

The transition to 4G does not mean there will be a change in interface technology, as there was in the shift from 2G to 3G. Instead, 4G promises to integrate different modes

of wireless communication, from indoor networks such as wireless LANs and Bluetooth,

to cellular signals, radio, TV broadcasting, and satellite communications (Steinbock,

2003).

Japanese operators have tried to speed up the transition from 3G to 4G. They hope to lead technology development in 4G and begin standardizing it by 2005 (Steinbock,

2003). Chairman Kouji Ohboshi of NTT DoCoMo, Japan’s largest cellular outfit, has said, “In my personal view, I would like to introduce 4G in about 2007” (Rea, 2001).

NTT DoCoMo has great success through its popular i-mode service. The Web access protocol on NTT DoCoMo’s terminal, i-mode, is the most successful mobile Internet access model in the worldwide telecommunication market (Ishii, 2004). With i-mode, subscribers can buy sodas from vending machines, purchase food at fast food restaurants, and shop at Internet retailers like Amazon.com, buying all their goods through

DoCoMo’s billing system (FierceWireless, 2004). However, old regulatory rules have continued to prevent its globalization. In 2002, NTT DoCoMo announced trials for its 4G technology in Japan. The company wants to have maximum transmission speeds of 100M bits/s, more than 200 times faster than the recently introduced 3G system (Steinbock,

2003).

90 82 80 72 70 60 51 45 April 03 50 41 40 39 43 41 40 32 32 June 02 25 24 30 19 June 01 20

% of mobile phone % mobile of 10 0 Europe North Japan Asia World America

Source: Deutsche Bank Research (2004)

Figure 2-3. Ownership of Internet Mobile Phone 2003

The vision for 4G mobile development is for a steady and continuous evolution

over the next 10 years. Beyond this time frame, sometime after 2010, future 4G systems

may be required for a new terrestrial wireless technology. To be successful, the key

drivers will have to effectively exploit this new environment, which will require a change

in the operator’s mindset and business model.

The pre-cellular and 1G era were about the US’s dominance. The 2G era was about

Euro-Nordic mobile leaders. The 3G era started with accelerating specialization and fragmentation of the industry value chain in Western Europe, Japan, the US, and China.

The 4G era has been initiated with dreams of pervasive computing, R&D collaboration,

and joint ventures. But, the closer the implementation gets, the tougher the efforts at

dominance and control, as it has been in the last 130 years of mobile evolution.

The Global Cellular Mobile Phone Market

The global market for voice service, including fixed and mobile, is rapidly

approaching $1 trillion (DTT, 2005b). Particularly Deutsche Bank (2004) who estimated

that in 2003 GSM mobile telecom services had 2.3 billion global subscribers and directly

generated about $426 billion in revenues in 2003. This rapid change in the mobile

market, and mobile’s increasing role in providing basic telecom services, has been driven

by the introduction of 2G wireless technologies. But because of their limited data

capabilities, 2G technologies are not well-suited for providing advanced services.

Three-G wireless technologies that are now being deployed around the world offer

great opportunities to customers. As of 2004, over 8% of the world’s mobile subscribers

use 3G services. The 3G subscriber number will continue to rise and exceed 1billion by

2010 (CDG, 2004). In terms of regional development, the Asia Pacific region boasts

more mobile users than any other region, followed by Europe, North America, Latin

America, and Africa/Middle East (UMTS Forum, 2003). As the market migrates to 3G,

CDMA-based technologies such as CDMA-2000 and W-CDMA will reach 730 million users in four years, accounting for 38% of the total wireless market. The market share of

2G GSM will drop to 62% (CDG, 2004b).

140 120 100 80 60

millions CDMA-2000 40 20 W-CDMA 0 Dec- Mar- Jun- Sep- Dec- Mar- Jun- Sep- Dec- Mar- Jun- Sep- 01 02 02 02 02 03 03 03 03 04 04 04

Source: CDG (2004b)

Figure 2-4. Numbers of Subscribers 2001-2004 (CDMA-2000/W-CDMA)

To ensure a successful transition to the 3G era, operators have to focus on practical applications of the technology, driven by real customer needs and usage patterns

(Deloitte, 2005). Operators should also approach the 3G consumer market with services that are incremental improvements over successful 2G services such as, Short Message

Services (SMS), Multimedia Messaging Service (MMS), downloadable ring-tones, images and games, news and information sources, mobile chat, and Internet-style portals

(UMTS Forum, 2003). These services are typically real-time, on-demand, and self-

provisioned by the customer; they cross physical and logical boundaries within the

operator, and rely on both old and new technologies from the telecommunications and

computer industries to work. Deloitte Touche Tohmatsu (DTT, 2005b) communications

forecasts that 3G will not achieve widespread adoption during 2005, but it will turn an

important corner on the road to ultimate success. Dozens of 3G networks will go live in

2005, and customer interest will initially be generally low due to poor handset

performance, disappointing network quality, and a lack of compelling benefits. But, it is

expected that mobile operators and vendors will likely be delivering a 3G experience that

is comparable to 2G, and that by 2006 3G handsets, networks, and services will be

noticeably superior to their 2G ancestors (DTT, 2005b).

Market evolution in the pre-cellular era shifted from industrial services to nascent

business markets and experimental consumer markets. With cellular technology such as

analog (FDMA) and digital (TDMA and CDMA) available, the market momentum

shifted from corporate services to mass consumer markets. As the mobile market

becomes saturated, segmentation strategies will become far more important and

sophisticated. Services and pricing will increasingly be personalized and operators will

seek lucrative opportunities for targeting specific ethnic groups and other tight-knit communities (DTT, 2005b). Functionalities have also evolved dramatically from the primitive pre-cellular technologies to analog, digital, and multimedia cellular, and the future will see broadband cellular (Figure 2-5; Steinbock, 2003)

Technology Development Products and Services 4G (2008/10-) Broadband Cellular

3G Multimedia Cellular (2001- 2008/10) 2G Digital Cellular (1992-2001) 1G Analog Cellular (1983-1992) Pre-Cellular FM (1946-1983) AM

Emergency Industrial Business Large-Scale Worldwide Market Services Services Market Consumer Consumer Thrust Market Market Source: Steinbock (2003)

Figure 2-5. Mobile Technologies, Market and Innovation

Based on the growth in mobile phone subscribers, mobile commerce (m-

commerce) has emerged as a new electronic commerce market (Kauffman &

Techatassanasoontorn, 2003). According to information provided by ITU (2001b), by

2005, $22.2 million of revenue will be generated globally as a result of transactions made

possible by mobile devices. Asia will be quick to adopt m-commerce generating revenues

of $9.4 million (nearly 60% of which will be led by Japan), followed by Western Europe generating revenues of $7.8 million, and more slowly followed by North America generating revenues of $3.5 million (nearly 94% of which will be generated by the US).

There are 3 kinds of m-commerce market players. Mobile network operators such as NTT

DoCoMo in Japan and Orange in the U.K. are best positioned to benefit from the introduction of mobile Internet service. They already have an established customer base and control the networks. Most operators aim to position themselves in a key role for m- commerce by owning the portal and participating in the revenues accrued by services over its network (Dursun & Gokbayrak, 2000). Suppliers include handset/network equipment manufactures, application system developers, and security specialists. They are critical in the value chain because, generally, customers do not shop for a particular service provider or network operator, but rather for the handset brand (Dursun &

Gokbayrak, 2000). The most famous players are phone makers such as Motorola, Nokia,

Ericsson, and NEC, who are making Wireless Application Protocol (WAP) enabled phones to support m-commerce (M-commerce, n.d.).

Content providers are made up of established businesses looking to add wireless access to their options. The content provider has the contract and billing relationship with the customer, but does not own any infrastructure. Control over the billing relationship

puts the service provider in the position to offer a wide variety of services like m-

commerce application by charging goods and services directly to the phone bill. The

network operator must provision for it (Dursun & Gokbayrak, 2000).

Where and how fast m-commerce will penetrate into the global markets is not yet

clear, but mobile phone usage and sales are elevating, outpacing both existing and forecasted PC sales (World Economic Forum, 2004). Right now, the numbers of global

Internet browsing users through mobile service are growing (Figure 2-6).

800 700 600 500 Mobile Access 400 Fixed PC Access 300

millions of users 200 100 0 1999 2000 2001 2002 2003 2004 2005

Source: M-commerce (n.d.)

Figure 2-6. Global Internet Browsing Users 1999-2005

Various Cellular Mobile Phone Market in the World

Different countries have different patterns of mobile phone diffusion. In particular,

some countries like Finland, Japan, Korea, and Hong Kong show evidence of a rapid

increase in the cellular mobile phone market, while others like India, China, and the US

have seen a more gradual increase in the cellular mobile phone market (Kauffman &

Techatassanasoontorn, 2003). Researchers claim various factors for the growth of cellular

mobile phone adoption and usage in different countries. In the case of Japan, The Garner

Group argues that the unique characteristics of Japanese culture, low PC penetration, and

the high cost of fixed line access charges provided the basis for the extraordinary growth

in the number of mobile phone users, which was 48% of the population in March of 2001

(Foong, 2001). In Europe, NMT is one of a few examples of how a strong European

regulatory policy in support of a uniform communications standard is instrumental in the

growth of cellular mobile phones.

DTT reports on the Global Telecommunication Index (2003-2004), which is based

on each country’s market capitalization, and comprehensive because it measures an

industry’s long term value (DTT, 2005a). Derived from share price movements, market

capitalization broadly reflects a company’s past, current, and expected future

performance, whereas revenues and profits only reflect performance at a single point in

the past. Because this report provides a regional telcos’ overview of the markets of the

Americas, EMEA (Europe, Middle East, and Africa) and the Asia Pacific, we can see

regional telecommunications performance between 2003 and 2004.

Europe, the Middle East and the African region

According to a DTT report, the telcos’ EBITDA (Earnings before interest, tax, depreciation, and amortization) margin in EMEA has improved dramatically from 17% to

40% in contrast to previous years. Cost reduction was the main driver, as well as the addition of high margin entrants such as Saudi Telecom (Saudi Arabia), Belgacom Group

(Belgium) and Telkom SA (South Africa). In Europe, numerous companies have

launched 3G services. The UK and Italy already boast millions of 3G subscribers.

Vodafone (UK), Orange (UK) and T-Mobile (UK) all launched 3G in some or all of their

European operations during the 4th quarter of 2004. At that time, many 3G networks

covered less than 70% of the population and suffered from poor in-building coverage.

Peculiarly, the more mature Western European economies, which faced high penetration

rates and exhibited relatively stagnant growth. The UK was the notable exception,

however, reporting significant growth in revenue largely due to Vodafone’s global

expansion. In the Middle East and Eastern Europe countries that have had low mobile

penetration (about 50%), showed impressive increases in subscribers. This trend is likely

to continue.

The top 10 telcos in the EMEA region are Vodafone (UK), Telefonica (Spain),

Nokia (Finland), Saudi Telecom (Saudi Arabia), Emirates Telecom (UAE), Telecom

Italia (Italy), France Telecom (France), BT Group (UK), TIM SpA (Italy) and Deutsche

Telekom (Germany). Vodafone remains the world’s largest mobile operator, although

China Mobile has twice as many subscribers. Vodafone’s advantage is that its 150

million subscribers are concentrated in developed countries. Vodafone has also launched

3G services in 13 countries, but they failed in their attempt to acquire AT&T Wireless, a move that would have given them branded property in the US. Vodafone currently owns

45 percent of Verizon Wireless, a good investment, but one that provides limited opportunities for control. Verizon Wireless’ CDMA network is also incompatible with most of Vodafone’s global network, which is predominantly GSM.

When compared with former DTT research, it appears that Nokia (Finland)’s revenue declined over the past 3 years. The company’s market share in 2004 plunged from roughly 40% to less than 30%, before bouncing back to 33% in the latter half of the year through aggressive price cutting. Despite the company’s heavy marketing, recent innovations like circular dial pads and a handset specifically designed for playing games have generally failed to impress consumers. In short, the aggregate market capitalization

of these top 10 companies at the end of 2004 was $733 billion, which constitutes 60% of

the EMEA region’s aggregate market value.

The Asia Pacific region

According to a DTT report, the Asia Pacific region was well-suited for EMEA.

With its expansive geography, diverse cultures, and varying degrees of governmental protectionism, the region is a melting pot of telcos with growth prospects. During 2003-

2004, Japan’s predominance declined over the period. While Japan holds their power in

the Asia Pacific market and the global market, the current level of competition in the

Japanese market is not sustainable. Korea’s telcos faced similar declines with few

significant growth prospects in the country’s mature domestic market. In contrast to

Japan and Korea, China became the largest mobile phone market (by subscribers) in the

world, and currently accounts for the greatest absolute number of new subscriber

additions of any market. Mobile telephony has driven China’s combined teledensity to

33% in just three years (Deutsche Bank 2004). This, together with its thriving

manufacturing capability, has commanded the attention of the key global mobile handset,

infrastructure, and software players. In the future, China’s mobile penetration should

grow significantly beyond its current level of 21%, with every 5% increase equivalent to

adding the entire UK population to the telcos market. China’s telcos are likely to seek

opportunities outside of their country, gradually becoming regional telcos. China Mobile

and China Unicom are particularly strong, ranking in the top three of the world’s largest

mobile operators (by subscriber number). However, World Bank indicated that Chinese

telecom markets have yet to be liberalized and deregulated (UN, 2004).

India and Pakistan reported a strong increase in subscriber growth. India in

particular has a population on par with China, and it has already established itself as a

29 leading supplier of IT outsourcing. While its mobile penetration rate of 2.5% is among the world’s lowest, its mobile subscriptions grew by 102% per annum over the last three years.

Growth in lesser known countries in Indo-China has been equally dramatic. In

Cambodia, mobile communications have grown a hundred-fold in a decade, exceeding fixed lines by more than 8 to 1. This is due to their increase in mobile subscribers, almost all of whom are on GSM. Vietnam’s mobile growth rate has also been impressive, at over

50% annually. By the beginning of this year there were 2 million subscribers, and there was an expected increase to 7 million by 2006 with GSM technology occupying 80% of the market (Deutsche Bank, 2004).

DTT reported that the top 10 companies’ total market capitalization at the end of

2004 was $370 billion, which constitutes 71% of the aggregate market value of the Asia

Pacific constituents. The Asia Pacific region’s top 10 telcos are China Mobile (China),

Sing Tel (Singapore), SK Telecom (Korea), NTT DoCoMo (Japan), Telstra (Australia),

KDDI Corp (Japan), NTT (Japan), Telecom Corp of New Zealand (New Zealand), KT

Corp (Korea) and China Unicom (China). Overall the 10 largest telcos in the Asia Pacific have been able to preserve their profitability in line with the global average. While cost reduction initiatives have been a major factor, incumbency and associated government protectionism have also been strong drivers. But, Japanese telcos NTT, NTT DoCoMo and KDDI were the notable exceptions. Intense competition and an excess supply of service providers led to significant pricing pressures and reduced profitability.

Currently, telcos in these mature Asian Pacific markets are looking for new ways to grow. Mobile penetration in Taiwan and Hong Kong already exceeds 100%. Other

30 developed countries such as Japan, Korea, Singapore and Australia are near saturation.

To achieve significant growth, companies in this market will most likely need to pursue regional opportunities beyond their own borders. Although a few companies such as Sing

Tel have begun to establish regional footprints, there is still significant room for a major pan-Asian acquisition or alliance.

The Americas

In the case of the Americas, DTT’s data from the current period shows that revenues have stabilized, but margins have declined. This is largely due to fierce competition with cable companies in the voice market, and declining revenues for equipment manufacturers. The US and Canada, which are mature markets, both exhibited modest subscriber growth. Brazil and Mexico exhibited strong growth in both dimensions as was expected from immature markets with low penetration. However, Chili,

Venezuela and Argentina reported surprisingly poor growth in revenue and subscribers, despite a low penetration level. Growth in the America’s was the lowest of all three regions.

In 2004, Cingular Wireless (jointly owned by SBC and BellSouth) completed its acquisition of AT&T Wireless to become the largest wireless provider in the US. Sprint announced its acquisition of Nextel and is currently seeking regulatory approval. Cable companies could also disrupt the wireless market if they decide to add wireless services to their bundle. Verizon Wireless is currently the second-largest provider in the US behind Cingular, which completed its acquisition of AT&T Wireless at the end of 2004.

DTT reported that Verizon will have a significant opportunity to expand its market share as Cingular and AT&T Wireless struggle to merge operations.

In late 2004, Sprint agreed to acquire Nextel. The new company will have to

confront significant system issues, particularly the issue of whether to keep Nextel’s

aging iDEN standard, or shift all customers to Sprint’s CDMA standard. The latter would

offer advanced data and video services. The total market share of these leading companies, Cingular, Verizon, and Sprint Nextel, is twice as big as companies like T-

Mobile (Chan-Olmsted, 2005).

Digital Divide between Developing and Developed Countries

Definitions of Developed and Developing Countries

The terms “developing” and “developed” countries are not labels assigned to

similar, specific problems. According to the United Nations Statistics Division

(UNSD, http://unstats.un.org):

In the United Nations system there is no established convention for the designation of “developed” and “developing” countries or areas. In common practice, Japan in Asia, Canada and the United States in North America, Australia and New Zealand in Oceania, and Europe are considered “developed” regions or areas. In international trade statistics, the Southern Africa Customs Union is also treated as a developed region and Israel as a developed country; and countries of eastern Europe and the former U.S.S.R. countries in Europe are not included under either developed or developing regions.

The World Trade Organization (WTO, www.wto.org) also defines these terms as follows:

There are no WTO definitions of “developed” and “developing” countries. Members announce for themselves whether they are “developed” or “developing” countries. However, other members can challenge the decision of a member to make use of provisions available to developing countries.

Wikipedia (http://en.wikipedia.org), an online encyclopedia, defines both

“developed” and “developing” countries. Usually, the term “developing” country is one that has the potential for economic strength, but lacks the skills, capital, or technical equipment to immediately exploit its own resources. A “developing” country often refers

32 to countries with low levels of economic development, but this is usually closely associated with social development in terms of education, healthcare, life expectancy, etc.

There is also a strong correlation between low income and high population growth, both within and between countries. The term “developed” countries often refers to countries with a relatively high standard of living through a strong, high-technology and diversified economy. Usually, these countries have an economic system based on continuous, self- sustaining economic growth. Most countries with a high per capita GDP are considered

“developed” countries.

Digital Divide and Leapfrogging

ICTs are powerful tools that can accelerate a country’s social and economic development. However, the opportunity to use and benefit from these technologies has been limited to those who live in a handful of developed regions and countries (Kauffman

& Techatassanasoontorn, 2005). Connectivity and access are significant obstacles to ICT in developing countries, despite the potential benefit they can offer. UN (2004) explains the underlying causes of low levels of ICT penetration in developing countries: 1) a lack of awareness of what these technologies can offer; 2) insufficient telecommunications infrastructure and Internet connectivity; 3) expensive ICT access; 4) absence of adequate legal and regulatory frameworks; 5) shortage of requisite human capacity; 6) failure to develop local language content; 7) and a lack of entrepreneurship and business culture open to change, transparency, and social equality. These obstacles bring about what is widely known as a “digital divide” between developed and developing countries.

Generally, digital divide has been identified as the dichotomy between those who have computers and Internet access and those who do not (Warschauer, 2003). The extent of the digital divide between developed and developing countries is enormous. According

to UN (2004), during 2001-2002, the top 30 ICT diffusion sites were dominated by

OECD and high-income countries. The US ranked first, with European countries making up the rest of the top ten except for Singapore, which ranked ninth. Australia maintained its rank at number ten, while Hong Kong, China slipped marginally from 11 to 12. South

Korea rose in the rankings from 22 to 11, followed by Japan at 17. El Salvador, Tanzania,

Bangladesh, and Cameroon are among those countries at the bottom of the rankings.

Recently in several countries, the digital divide has decreased thanks to governmental or private programs that have put computers in poor and rural schools, and hooked them up to high-speed Internet. In the mobile sector, the distribution of mobile phones across countries also reflects more equal access to mobiles in the “leapfrogging” and “catch-up” noted by observers (UN, 2004). Leapfrogging (or the Leapfrog Effect) is a theory of development in which developing countries skip inferior, less efficient, more expensive, or more polluting technologies and industries to move directly toward more advanced ones (Wikipedia, n.d.). Technology leapfrogging offers an opportunity for developing countries to catch up with global ICT trends. Steinmueller (2001, pg.194) defines leapfrogging as “bypassing stages in capacity building or investment through which countries were previously required to pass during the process of economic development.” Developing countries generally can’t afford to create or rebuild basic infrastructure for new technology. However, due to the lack of investment in legacy systems, hardware, and software, they can be in a good position to leapfrog over some of these incremental steps. If the degree of digital divide is reduced in the cellular mobile phone sector, the leapfrog effect might occur in certain countries. In fact, there is every probability of observing the leapfrog effect in the world’s cellular mobile markets.

Since Internet technology makes it easier to develop ICT services, current technologies offer great potential to developing countries looking to introduce ICT into their region (Steinmueller, 2001). We can see examples related to the Internet in several

regions. In Nigeria, telephone lines are unreliable and limited in their transmission

capabilities. The Africa ONE project is attempting to connect Lagos (Nigeria’s economic

and commercial capital) to Europe with ultra fast fiber optic cables buried underneath the

Atlantic Ocean. Through this cable, Nigerians can use the Internet by making a local telephone call, instead of using an unreliable telephone system (Emeagwali, 1999).

Internet service provider VSNL controls all the international telecommunications in

India, but its telephone lines are badly clogged, inadequate resources that limit Internet access. Instead of installing new telephone lines, India developed their cable TV network for Internet access, a move that will provide the country with much opportunity (Joshi,

1999). In the South Pacific area, there is a large gap between urban and rural areas. Urban

areas have seen a significant growth in Internet use, while some rural villages such as

Samoa do not even have telephone access. Wireless Internet would be an ideal method of

bringing ICT resources to people of this area. Using wireless Internet booth Telecentres

and Internet kiosks, people could access the Internet and search for information without telephone lines (Toland & Purcell, 2002).

Countries that advance from having no telephones to having cellular phones skip the stage of landline telephones altogether (UN, 2004). Lee and Lim’s study (2000) found three different patterns of “catching-up” in Korean Industries. In the first two cases, path- creating catching-up in CDMA mobile phones and path-skipping catching-up in D-RAM and Automobile proved to be leapfrogging. When the Korean firm and the government

authorities considered the development of the cellular mobile phone system, the analog

system was dominant in the US and the GSM system was the dominant system in Europe.

However, the Korean authorities paid attention to the emerging CDMA technology. In

fact, if Korea just followed the already established GSM, the gap between Korea and its leading countries would take even longer to close. Korea chose a shorter but riskier path and finally had success. In 2002, China’s fixed line and mobile networks were the largest in the world (DTT, 2005a; UN, 2004), but on a per capita basis relative to population,

China’s telecommunications networks remain average among low and middle-income countries. According to UN (2004), China had 17.1 main fixed lines per 100 inhabitants,

16.0 mobile subscribers per 100 inhabitants, and 2.7 PCs per 1,000 inhabitants in 2002.

These rates lagged behind several other East Asian countries including Singapore and

Malaysia. However, China has had very high growth rates in ICT infrastructure in recent years. In 2002, China had among the highest growth rates in main fixed lines (19%) and

PCs (42%) in East Asia. Internet connectivity is also growing fast. The number of

Internet users grew five times from 22.5 million in 2001.

Wireless networks are much simpler and cheaper to install than fixed line systems

(Fleming, 2003). The even distribution of mobile telephones suggests greater access to mobile communications in developing countries. Mobile phones could also be an important technology where challenging geography prevents access with mainline infrastructure to remote regions. However, wireless technology is not a universal remedy.

It is still difficult to reduce the gaps between developed and developing countries in mobile technology access. Introducing more advanced mobile technology is a considerable matter in developing countries. One problem hindering the successful

introduction of future 3G in developing countries is the relatively higher cost of 3G service than 2G service for consumers in those countries (ITU, 2001a). This is probably the largest single barrier to the effective development and use of new telecommunications technologies. Increasing the number of users on the new network and spreading the fixed costs of services is one way to reduce the average cost of services. However, this will not be easy to do in economic circumstances that are likely to support only limited market demand.

Theoretical Background

Most studies in the adoption, diffusion, and usage of new communication media have emerged from several research streams: diffusion of innovations, media choice, and the implementation of information systems (Rice & Webster, 2002). Among these, the diffusion of innovations suggests that characteristics of an innovation and

communications network influence media diffusion.

This study is based on the assumption that the cellular mobile phone is an

innovation technology that uses the diffusion of innovation theory as its theoretical

framework. According to diffusion theory, adoption and diffusion of innovations are a function of one’s innovativeness, or willingness to try new products. Therefore, if we consider cellular mobile phones as an innovation, diffusion theory may offer clues about countries that are relatively early or late to adopt cellular mobile phones and their technology.

Diffusion of Technological Innovation Theory

According to Rogers (1995), diffusion of innovation can be defined as the process

by which an innovation is communicated through certain channels over time among a number of social systems. He identifies five adopter categories: innovators (2.5%) are the

earliest adopters, followed by early adopters (13.5%), the early majority (34.0%), the late

majority (34.0%), and finally laggards (16.0%), who are the latest adopters.

The theory focuses on communication channels, the stages of awareness and

decision-making, the criterion for decision-making, and the characteristics of potential

adopters. Typically, a sequential stage model consisting of adoption and implementation phases are assumed (Cooper & Zmud, 1990; Fichman & Kemerer, 1997).

Innovation generally follows an S-shape curve of adoption. Adoption starts off slowly, then rises quickly as more and more users adopt an innovation and finally levels

off towards the end of the diffusion process (Rogers, 1995). Several researches have tried

to explain the S-shaped nature of the diffusion process. Griliches (1957) proposed an

“epidemic” model for the emergence of an S-shaped diffusion curve. Mansfield (1961)

sought to explain the observed patterns of diffusion in terms of the expected profitability

of the innovation and the dissemination of information about its technical and economic

characteristics. Rogers (1995) employed a communications-based model for explaining

diffusion patterns.

According to Rogers’ model (1995), an innovation process is referred to as the

process through which an individual passes from knowledge of an innovation, to attitude

formation (persuasion), to making a decision to adopt or reject the innovation, to

implementation of new media, and finally to the confirmation of this decision. The

Rogers model addresses all stages associated with the adoption of an innovation.

Specifically, the first three stages, knowledge, persuasion, and decision, are usually

designated as the adoption decision process in that they involve the decision sequence

that consumers use to determine whether or not to adopt the innovation. These stages in

the adoption process incorporate the effect of personality type, experiences, and attitudes

on the adoption decision. In this aspect, the adoption decision process is very useful in

explaining how to adopt a new medium that does not yet exist in the marketplace.

Past studies have sought to explain diffusion of innovation based on the adopter’s characteristics, the social network in which the adopters belong to, the communication process, the characteristics of the promoters, and the attributes of the innovation itself including triability, relative advantage, compatibility, observability, and complexity

(Cooper & Zmud, 1990, Davis, Bagozzi, & Warshaw, 1989; Fichman & Kemerer, 1997).

At the macro-level, scholars sought to explain the diffusion of innovation at an industry level by looking at the characteristics of innovative firms (Dosi, 1988), the absorptive capacity of individual companies (Cohen & Levinthal, 1990), or the process by which dominant designs emerge (Anderson & Tushman, 1990).

Although there are few country-level diffusion studies that focus on finding significant factors in different diffusion regions, most studies of international product diffusion across countries are based on the diffusion model of Bass (1969). Dekimpe and

Parker (2000), Gruber and Verboven (2001), and Takada and Jain (1991) reported the influence of country characteristics. This Bass model also suggests that the adoption of a

new technology is determined by external factors such as government policies, mass

media communications, the level of competition, the number of standards, and internal

factors such as word-to-mouth communications (Gruber & Verboven, 2001;Dekimpe &

Parker, 2000; Rai, Ravichandran, & Samaddar, 1998; Robertson & Gaignon, 1986).

Dekimpe and Parker (2000) propose a new methodology called the “coupled-hazard

approach” to study the global diffusion of technological innovations. They suggested the

39 conceptual difference between the timing of a country’s introduction to the new technology—the so-called implementation stage (Rogers, 1995)—and the timing of the innovation’s full adoption in the country (the confirmation stage). Gruber & Verboven

(2001) studied the technological and regulatory determinants of the diffusion of mobile telecommunications services in the European Union. They found that the effect of technological transition and each country’s policy-based factors had a major impact on the diffusion of technological innovation. Rai, Ravichandran, and Samaddar (1998) found that the internal influence diffusion models assume that the diffusion rate is driven by interactions between existing and potential adopters in a social system. Robertson and

Gatignon (1986) argue that the sooner the industry reaches agreement on a dominant standard, the more rapid the diffusion process.

Although the traditional diffusion of innovation theory has provided many useful insights into understanding the adoption and diffusion of technologies in the past, recent work in the diffusion of complex technology points out its limitations (Lyytinen &

Damsgaard, 2001; Tuomi, 2002). In particular, traditional diffusion of innovation studies treat innovation as a distinct and measurable feature (Rogers, 1995). Thus, the innovations are often characterized as unproblematic, complete, unambiguous objects that need to be “diffuse” as they are in a linear temporal sequence. However, advanced technologies, such as broadband mobile computing services, are ambiguous, problematic, messy, and flexible (Yang, Yoo, Lyytinen, & Ahn, 2003). Such advanced technologies have interpretative flexibility (Bijker, 1995; Orlikowski, 1992). As such, actors who belong to different communities construct different meanings from the innovation.

Therefore, these technologies are socially constructed, and simultaneously, community-

40 shaping (Hughes, 1987). Therefore, the study of innovation and the diffusion of advanced technological innovations like broadband mobile computing services must address several scientific, public, and economic factors ranging from the observation of ideas, theories and laboratories, and industrial policy and regulation, to explorations of marketing strategies and changes in consumer behavior. Most of these features are addressed separately and are isolated from other lines of research. Though each issue mentioned above is useful in understanding a specific phase or aspect in the innovation and diffusion process, each one alone is inadequate to account for the dynamic evolution of advanced technical systems.

Factors Affecting the Diffusion of Cellular Mobile Phone Technology

Researchers propose that various factors influence the adoption and diffusion of new media and technologies at the country level. To find the factors affecting mobile phone development, I will first review literature that details the factors affecting technology as well as new product adoption and diffusion at the individual, firm, and country levels. Then I will examine literature about mobile phone studies that address the drivers for the growth of mobile communication.

Individual, Firm, and Country-Level Adoption and Diffusion

According to Lin (2003), the determinant of individual level adoption is influenced by s the following factors: 1) predisposed personality traits that make the audience receptive to the idea of innovation adoption, such as risk tolerance; 2) the self-actualization needed for adoption such as for work or pleasure; 3) beliefs about one’s ability to adopt and use a technology innovation with companies; 4) and beliefs and attitudes about the rationale for innovation adoption. For example, innovative voice email users are more capable of utilizing the technology’s ability to provide and obtain useful

41 information in an organizational setting (Rice & Shook, 1990). Similarly, the ability for greater innovative thinking and the perception of the relative advantage of computer technology are predictive of personal computer adoption decisions (Dickerson & Gentry,

1983).

Studies have investigated firm-level adoption determinants of specific technological advantages (Mahler & Rogers, 1999; Goel & Rich, 1997; Hannan &

MacDowell, 1984). Hannan and McDowell (1984) performed an elaborate study of the firm-level adoption of automated bank teller machines. Along with other impressive results, they found evidence that the regulatory environment in which banking firms operate shapes their decisions regarding the adoption of new technology. Mahler and

Rogers (1999) proposed that the most important obstacle to the adoption of new telecommunications services by banks is a low degree of diffusion caused by the critical mass. Goel and Rich (1997) investigated firm-specific conditions and proposed that product market competition and prior adoptions are found to be important determinants of technology adoption in the airline industry.

There are several studies focused on the specific factors of technology diffusion at the country level. Most studies generally emphasize economic, technological, and regulatory/policy factors. Previous research has indicated that countries with a higher standard of living usually promote fast technology adoption (Kiiski & Pohjola, 2002;

Gruber & Verboven, 2001; Dekimpe & Parker, 2000). Norris (2003) found that the faster an economy developed, the stronger Internet growth was achieved in a given country.

There is also a revealing trend that higher income groups tend to have greater access to the Internet, and that the number of Web hosts in a given country is congruent with

individual income. Beilock and Dimitrova (2003) also indicated that per capita income

was the most important determinant of global differences in Internet usage rates. Digital

mobile phone technology requires large investments in switches, networks, and operation

license acquisition before any services can be offered (Kauffman &

Techatassanasoontorn, 2003).

Technology Infrastructure Factors

Technology infrastructure is another main determinant of Internet, computer, and

mobile diffusion. Kelly and Petrazzini (1997) indicated the influence of technological

infrastructure when explaining the large differences between connectivity among

countries of different income brackets. Moffett (1997) found that an inadequate telecommunications infrastructure is a major obstacle hindering Internet development in

Central Asia. There are not enough telephone lines in the main cities of Kazakhstan,

Uzbekistan, Tajikistan, and Turkmenistan, and insufficient links with rural areas impede

the Internet diffusion in Central Asia. Uimonen (1999) asserted that adequate

infrastructure is crucial to Internet growth. In the case of Laos, which was one of the last

countries in Asia to establish Internet connectivity, the slow development of the Internet

is explained by low levels of personal computer penetration and a poor

telecommunications network, coupled with a lack of awareness among users, developers,

and decision makers. Gruber and Verboven (1999) noted that technological developments

and the implied changes in capacity and quality may explain global increases in the

number of mobile subscribers. Hughes (1988) studied the diffusion of the telephone in

Germany and concluded that the spread of technology is contingent upon the presence of

certain technological and infrastructural factors in a given country.

Cadima and Barros (2000) studied the diffusion of cellular mobile phone technology on a pre-existing fixed line telephone service. The result suggested that mobile phone diffusion has a negative effect on the rate of fixed line telephone penetration, reflecting an approximate 10% decrease compared to the absence of mobile phones. On the contrary, Gruber and Verboven (1999) examined the effect of fixed line telephones on mobile phone diffusion, and I also found a negative coefficient. These two studies show that substitution affects both mobile and fixed line telephone networks.

However, despite the apparently natural effect of substitution on the number of subscribers between mobile telephone and fixed line telephone services, the empirical result is not totally clear, as Ahn and Lee (1999) found demand for access to mobile phones to be positively related to fixed line penetration.

Many previous studies indicate that the adoption of new product is related to the adoption of other innovations (Lin, 1994; Jeffres & Atkin, 1996). Reagan (1987, 1991) found that the adoption of telecommunications technologies was predicted by the use of other similar technologies and user attitudes toward them. This adoption pattern of functionally similar products might be stimulated by the acquisition of a “trigger” innovation (Dozier, Valente, & Severn, 1986), such as the computer. Dickerson & Gentry

(1983) reported that experience with other computer-related products and services plays an important role in the purchase of PCs. Lin (1998) indicated that computer adoption was related to Internet adoption as well as a technology adoption. Kang (2002) concluded that digital cable subscribers are more likely to watch television. Media usage has been studied as a potential factor affecting technology adoption, but the results are inconclusive. Becker, Dunwoody, and Rafaeli (1983), and Rothe, Harvey, and Michael

(1983) found that cable television subscribers spend more time with other media.

However, Lin (1994), Jeffres, and Atkin (1996) concluded that media use is irrelevant to

the adoption of innovations such as video text and the Internet.

Regulatory and Policy Factors

Regulation/policy is a strong factor that might explain the diffusion of technology

among countries. Fan (2005) tried to identify the regulatory factors that encourage the creation of a policy and the regulatory environments favorable to the development of

Internet infrastructure and access in China and Australia. They found that the most important factor is the level of competition, and suggested that deregulatory mechanisms and interconnection regimes are needed to establish a more competitive environment for

Internet access in both countries. Gruber and Verboven (2001) proposed that regulatory delays caused a relatively slow diffusion convergence between 140 countries and that introducing competition has a strong impact on diffusion. Similarly, Hyun and Lent

(1999) suggested that Korea’s strengthened and expanded telecommunications infrastructure is mainly due to major restructuring driven by the Korean government.

Cultural Factors

Culture is another factor that might affect the diffusion of technology at the country level. Ferle, Edward, and Mizuno (2002) examined two leading countries, the US and

Japan, in an attempt to understand the underlying factors influencing Internet diffusion.

They assumed that the diffusion of the Internet might be similar between these two countries. However, Internet penetration rates were different in the US and Japan, as these two countries are quite different culturally. Takada and Jain (1991) found that

“high-context” versus “low-context” cultures can impact the rate of new product

diffusion. High-context cultures experience a faster rate of adoption (Inoue, 1996) and

45 tend to be more homogeneous with a greater inclination towards group orientation (Ferle,

Edwards, & Mizuno, 2002).

Demographic Factors

Demographic factors are also noted as main drivers in the diffusion of technology at the country level. Atkin, Jeffres, and Neuendorf (1998) compared Internet adopters and non-adopters based on their social status, communications needs, media-use habits, and their relationships with technology. They found that demographic factors and technology usage provide a portrait of a medium in its early stages of diffusion. Demographic factors are also noted for their importance in the adoption of technology through many other media studies such as video text (Ettema, 1984) and computer bulletin boards

(Garramone, Harris, and Anderson, 1986; Rafaeli, 1986).

Individual knowledge may affect the spread of a communications technology, as well. Kelly and Petrazzini (1997) suggested that wealth, education, language, and pricing are important determinants of Internet connectivity. Similarly, using the Human

Development Index (HDI) measure from the UNDP’s Human Development Report,

Hargittai (1996, 1998) proposed that a country’s human-development level is proportional to its level of Internet connectivity. Laponce (1987) suggests that some languages are awarded greater status than others. For example, the English language is more prominent in the computer and media industry. Considering the dominance of the

English language on the World Wide Web, one's level of English proficiency may affect his or her use of the medium. This is not due to a higher rate of diffusion in the US, but rather the relative size of the US population compared to other countries (Hargittai,

1999). Kiiski and Pohjola (2002) showed that, in a sample including developing and

OECD countries, additional education had a positive and statistically significant

46 influence on ICT diffusion. Rogers (2000) noted that the Internet has been diffused primarily in urban areas among the comparatively wealthy and educated. Kelly and

Petrazzini (1997) also suggested that academic institutions play an important role in spreading the Internet.

Other Integrated Studies

In an integrated study, Bazar and Bolich (1997) identified several factors pertinent to the diffusion of the Internet in developing countries, including infrastructure, government policies and regulations, economic development, culture, language, and IT penetration. Xiaoming and Kay (2004) examined the relationship between Internet development and various social, economic, and political factors that are supposed to affect Internet growth in 28 Asian countries. Their findings show that the Internet penetration rate is directly related to a country’s wealth and telecommunications infrastructure, as well as the urbanization and stability of the government. Political freedom, high literacy levels, and English proficiency were not found to be related to

Internet diffusion. However, using data from developing countries, Baliamoune-Lutz’s study (2003) proposed that political rights, civil liberties, and economic openness to international trade are important determinants of the behavior of the mobile phone.

Baliamoune-Lutz (2003) also examined the links between ICT diffusion, comparing

Internet, personal computer, and mobile phone users and hosts with income, trade, financial, and educational indicators. In addition, Fernandez-Maldonado (2001) noted that economic difficulty, the high price of subscription to ICT, and political instability are factors preventing ICT diffusion in Lima, Peru. Ryu, Kim, and Kim (2003) explained that the success experienced by Korea in the roll out of high speed Internet access is due to the competition between companies, different technologies, and infrastructures. There are

multiple companies competing with their own broadband infrastructure, and the Korean

government’s strong intervention policy resulted in a massive diffusion of broadband.

Moreover, foreign capital investment and existing educational institutes are also

considerable factors that affect ICT diffusion. Hargittai (1999) found that economic

wealth and telecommunications policy/regulatory factors are the most significant

determinants of a country’s Internet connectivity in OECD countries. Guillen and Suarez

(2001) analyzed 141 countries and suggested that cross-national differences in the

number of Internet users and hosts have to do with favorable conditions for

entrepreneurship and investment.

Proposed Variables and Hypotheses

This study aims to collect and test a comprehensive list of factors that might affect

the cellular mobile penetration rate at the country level over a 7 year-period (1996-2002).

Based on former studies and in consideration of data usability, I will suggest several

independent variable groups, including economic development, economic freedom,

international business/trade openness, technological infrastructure, consumer-related factors, political environment, cultural factors, and market size. Because this study will use the panel data over a seven- year period, I will control the variance of the “year” and

“country” factors during statistical analysis to examine the effect of time on the diffusion of mobile phones by country. The following will review each of these aspects and the proposed related hypotheses.

Economic Development Factors

Factors related to economic development have been the most widely used and accepted factors affecting the diffusion of technological innovation. A country’s overall economic strength will affect the diffusion of mobile phones and technology because the

resources necessary for diffusion are more likely to be present and the capital required for

the expansion of technology is more available in wealthier countries. Therefore, wealthier countries may have a greater advantage in introducing technology sooner than less-wealthy countries. Economic factors are generally assessed by GDP per capita,

consumer expenditures, and foreign direct investment (FDI) in each country

(Chan-Olmsted, 2005; Baliamoune-Lutz, 2003; Kauffman & Techatassanasoontorn,

2003; Madden & Coble-Neal, 2003; Gruber & Verboven, 2001; Bedi, 1999; Gruber &

Verboven, 1999; Madden & Savage, 1998; Joseph & Drahos, 1996). FDI is the

movement of capital across national frontiers in a manner that grants the investor control

over the acquired assets (Wikipedia, n.d.). While the GDP per capita data describes the

relative wealth of a country (Madden & Savage, 1998), FDI is another important factor

that illustrates the attractiveness of a domestic market in the international community.

Inward FDI usually allows recipient economies access to advanced technologies,

managerial skills, and higher levels of knowledge (Baliamoune-Lutz, 2003). Bedi (1999)

indicated that because of the limited role of ICTs in enabling access, trade or foreign

investment may be a means of disseminating information and knowledge. Joseph &

Drahos (1996) also pointed out that the relaxation of limitations on FDI in

telecommunications is very much in line with liberalization and competition in the

telecommunications market. According to these studies, we can expect higher inward

FDI to contribute to ICT diffusion.

Finally, the level of income inequality (Gini index) in a country is an important

economic factor in the diffusion of technology (Hargittai, 1999). The higher the level of

income equality, the more people will be able to afford new technology, thus increasing

the probability of diffusion. Based on these findings, and controlling for country and year,

I hypothesized the following:

• H1:The higher the level of a country’s GDP per capita, the higher its level of cellular mobile phone penetration. • H2: The higher a country’s FDI, the higher its rate of cellular mobile phone penetration. • H3: The lower a country's Gini index, the higher its rate of mobile penetration.

Economic Freedom

Scully (1992) analyzed various measures of inequality and liberty in a sample of 70

countries and concluded that income is more equally distributed within countries that are

politically open, that have private property and market their allocation of resources, and

that are committed to the rule of law than in countries where these rights are abridged.

More specifically, Scully found that a significantly higher share of a free country's

national income goes to the middle class, while less income is received by the wealthy. In

a former study, income and economic freedom play an important role in the diffusion of

Internet users at the country level (Baliamoune-Lutz, 2003). This study uses the Heritage

Foundation's (www.heritage.org) Economic Freedom Index, which takes the average

score of ten indexes measured on a one-to-five scale, with five indicating the lowest level

of economic freedom. Based on these findings, and controlling for country and year, I hypothesized the following:

• H4: The lower a country's Economic Freedom Index, the higher its rate of cellular phone penetration.

International Business Trade Openness

As reviewed above, the role of trade policy and openness to international trade is an important indicator of technological diffusion. East Asian countries mainly promote their

ICT production through trade and export-oriented investments (Jussawalla, 1999). Both

exports and imports may offer a channel for increased adoption and diffusion of ICT.

Some imported goods and services require the existence of specific ICTs to be

operational. In some cases, ICT may be embodied in the imported product. Similarly, to

enhance their exports, firms find it increasingly necessary to make use of ICT. Mobile

phones, Internet use, and computerized operations are all tools used to improve the

efficiency of conducting business in the global market. These tools tend to reduce the

level of inaccurate information and incomplete market data (Baliamoune-Lutz, 2003). As

argued by Stiglitz (1989), the dissemination of inaccurate information results in less trade. Thus, we can expect a positive and significant correlation between ICT diffusion

and trade openness. In previous studies, trade openness proves to be an important

determinant in the diffusion of cellular mobile phones and personal computers at the

country level (Baliamoune-Lutz, 2003). Based on these findings, and controlling for

country and year, I hypothesized the following:

• H5: The higher a country's openness to International business trade, the higher its rate of cellular mobile phone penetration.

Technological Infrastructure Factors

As mentioned earlier, many studies indicate that the factors that make up a

country's technological infrastructure, such as main fixed lines, the number of computer

users, and the number of Internet users, are the main drivers of ICT diffusion at the

country level (Kelly & Petrazzini 1997; Moffett, 1997; Uimonen, 1999; Gruber &

Verboven, 1999; Hughes, 1988; Cadima & Barros, 2000; Ahn & Lee, 1999; Reagan,

1987, 1991; Lin, 1998; Kang, 2002; Becker, Dunwoody & Rafaeli, 1983; Rothe, Harvey

& Michael, 1983; Lin, 1994; Jeffres & Atkin, 1996). Given the results of those studies, I

hypothesized the following:

• H6: The higher the level of fixed line penetration in a country, the higher its rate of cellular mobile penetration. • H7: The higher the level of PC penetration in a country, the higher its rate of cellular mobile phone penetration. • H8: The higher the level of Internet penetration in a country, the higher its rate of cellular mobile phone penetration. • H9: The higher the level of television penetration in a country, the higher its rate of cellular mobile phone penetration.

Few studies have examined whether or not the cost of a fixed line telephone call significantly affects ICT diffusion including mobile phone diffusion. Logically, consumers consider price when making choices in the economic market. Consumer demand for goods and services is affected by their utility to consumers and the price at

which the goods and services are available (Picard, 1989). According to Picard (1989),

demand is a measure of the quality of goods and services that consumers are willing to

purchase at a given price. Thus, demand is dependent upon the willingness and ability of

a buyer to pay a particular price for goods and services. In this respect, the quality of

mobile services is usually higher than fixed line services. Furthermore, if the cost of fixed

line services is relatively high, we can expect consumers to select mobile services. Based

on these findings, and controlling for country and year, I hypothesized the following:

• H10: The higher the cost of land line telephone calls in a given country, the higher its rate of cellular mobile phone penetration.

ITU suggests the Digital Access Index (DAI) as a useful indicator for measuring

ICT diffusion. DAI is the first global index to rank global ICT access. It concentrates on

factors that have an immediate impact on determining an individual’s potential to access

ICTs. DAI includes eight variables covering five areas to provide an overall national

score. The areas: 1) availability of infrastructure; 2) affordability of access; 3)

educational level; 4) quality of ICT services; and 5) Internet usage. Based on these findings, and controlling for country and year, I hypothesized the following:

• H11: The higher a country's DAI index rating, the higher its rate of cellular mobile phone penetration.

Consumer-Related Factors

Social demographics such as population age and urbanization are also known to

affect consumer choices. Previous studies support that demographic factors play a

significant role in ICT diffusion, including mobile phone diffusion (Atkin & LaRose,

1994; Dutton, Rogers, & Jun, 1987; Krugman, 1985; Auter and Adams, 2004; Lin, 1998;

Dickerson & Gentry, 1983; Atkin, Jeffres & Neuendorf, 1998; Ettema, 1984; Garramone,

Harris, & Anderson, 1986; Rafaeli, 1986). In addition, Auter and Adams (2004) noted that, although mobile phone penetration in the US has increased, user demographics have changed. Women now represent the majority of US cell phone users, and the mean age and income level of cell phone users has declined in recent years (Robbins & Turner,

2002). The shift from business to personal communication is the primary reason for this trend (Robbins & Turner, 2002). Those who are most likely not to adopt cell phones now

or in the future tend to be much older, have a lower education, and have no children

(Leung & Wei, 1999; Robbins & Turner, 2002). Based on these findings, and controlling for country and year, I hypothesized the following:

• H12: The higher a country's population of younger people, the higher its rate of cellular mobile phone penetration.

Rogers (2000) noted that Internet diffusion has occurred mainly in urban areas

among the comparatively wealthy and educated. He also indicated that much of the

infrastructure needed for rapid diffusion of the Internet is not found in the rural areas in

India. Many villages in India do not have central electricity or telephone service and no

one in these rural areas can afford to own a computer. Based on these findings, and

controlling for country and year, I hypothesized the following:

• H13: The larger a country's urban population, the higher its rate of cellular mobile phone penetration.

Most studies examining the education level of adopters of new technologies find

that people with more education are quicker to adopt new innovations than people with

comparatively less education (Kelly & Petrazzini, 1997; Hargittai, 1996, 1998; Laponce,

1987; Hargittai, 1999; Kiiski & Pohjola, 2002; Rogers, 2000). Low education and literacy

levels are expected to hinder both the accessibility and dissemination of ICT

(Baliamoune-Lutz, 2003). Based on these findings, and controlling for country and year, I

hypothesized the following:

• H14: The higher the level of education in a country, the higher its rate of cellular mobile phone penetration.

Political Environment

Guillen and Suarez (2001) measured the relationship between entrepreneurship and

investment, and the number of Internet users and hosts using Henisz’s (2000) index of

political constraints. This index focuses on the degree to which a country’s political

institutions and the preferences of its elected and appointed officials constrain any one

political actor from introducing a sudden change in government policy. This index

approximates the credibility of an existing policy regime, indicating the extent to which

entrepreneurs and investors can take business conditions for granted and expect no

change. In short, this measures the extent to which there is predictability in policymaking

(Guillen and Suarez, 2001). Generally, democratic societies enforce more constraints

than less-democratic societies. Based on these findings, and controlling for country and

year, I hypothesized the following:

• H15: The more political constraints imposed by a country, the higher its rate of cellular mobile phone penetration.

Du (1999) and Goonasekera (2001) suggest that ICT development tends to face

resistance in countries with stronger political constraints, as these governments are more

likely to see the ICT as a threat to their political control. Buchner (1988) found that in

democratic countries the diffusion of telephones is more rapid than the diffusion of

television. They concluded that democracy encourages the growth of media that users can

use at their own discretion, and democracy also provides for a stable and predictable

institutional framework that fosters entrepreneurial activity and investment. In

Baliamoune-Lutz's study (2003), the diffusion of mobile phones and Internet use was

strongly associated with greater political rights and civil liberties. Published by Freedom

House (2001), the Index of Political Rights and the Index of Civil Liberties can measure

whether or not countries with higher levels of civil and political freedoms experience

more ICT diffusion. Political rights enable people to participate freely in the political

process, or the system by which the polity chooses policy makers and attempts to make binding decisions that will affect country, regional, and local communities. In a free society, this means the right for all adults to vote and compete for public office, and for elected representatives to have a decisive vote on public policies. A system is genuinely free or democratic to the extent that the people have a choice in determining the nature of the political system and its leaders. Civil liberties protect people from the power of government. Examples include the right to life, freedom from torture, freedom from slavery and forced labor, the right to privacy, the right to a fair trial, freedom of speech

and freedom of assembly, and the right to marry and the right to have a family. These are

usually guaranteed and protected by a constitution or by adherence to an international

treaty. This study uses the Political Freedom Index and Civil Rights Index from Freedom

House (www.wipo.int). Both indexes are measured on a one to seven scale, with seven

indicating the lowest degree of freedom. Based on these findings, and controlling for

country and year, I hypothesized the following:

• H16: The lower a country's political freedom index (both political rights and civil liberties), the higher its rate of cellular mobile phone penetration.

Cultural Factors

In the literature of new product diffusion rates, various cultural factors associated

with a variety of countries influence the diffusion process (Ferle, Edwards & Misuno,

2002). Western European countries and the US are industrialized and economically stable

countries that lead the world in the greatest number of mobile cellular phone subscribers.

However, there is significant difference between the levels of mobile phone penetration

in these countries. So, we can assume that cultural variables might play a role in affecting

the degree to which mobile cellular phones are adopted in different countries.

Many researchers have looked to Hofsted’s (1997) five cultural dimensions to

search for underlying factors impacting the general diffusion process of new innovations

(Tanaka & Jain, 1991; De Mooij, 1998; Herbig & Miller, 1991; Samiee, 1998; Tellefson

and Tanaka, 1999). Hofstede’s five cultural dimensions consist of the following: 1)

Individual/Collectivism; 2) Masculinity/Femininity; 3) High/Low Uncertainty

Avoidance; 4) Strong/Weak Power Distance; and 5) Long-Term Orientation. Previous research reveals that the level of cultural individualism versus collectivism and the level

of uncertainty avoidance have been found to affect diffusion rates (de Mooji, 1998;

Herbig & Miller, 1991).

Individualism focuses on the degree to which a society reinforces individual or collective achievement and interpersonal relationships. A high individualism ranking

indicates that individuality and individual rights are paramount within a given society.

Individuals in these societies may tend to form a larger number of looser relationships. A

low individualism ranking typifies societies of a more collectivist nature with close ties

between individuals. These cultures reinforce extended families and collectives where

everyone takes responsibility for fellow members of their group.

The uncertainty avoidance index focuses on the level of tolerance for uncertainty

and ambiguity within the society. A high uncertainty avoidance ranking indicates that a

country has a low tolerance for uncertainty and ambiguity. This creates a rule-oriented

society that institutes laws, rules, regulations, and controls in order to reduce the amount

of uncertainty. A low uncertainty avoidance ranking indicates that a country has less

concern about ambiguity and uncertainty and more tolerance for a variety of opinions.

This is reflected in a society that is less rule-oriented, that more readily accepts change,

and that takes more and greater risks. Based on these findings, and controlling for country

and year, I hypothesized the following:

• H17: The lower the level of individualism in a country, the higher its rate of cellular mobile phone penetration. • H18: The higher the level of uncertainty avoidance in a country, the higher its rate of cellular mobile phone penetration.

Market Size

Some of the most populous countries in the world account for a large proportion of the improvement in ICT diffusion. Between 1999 and 2002, China was the most

outstanding performer (World Economic Forum, 2004). China posted the highest increase

in the number of main fixed lines, cellular mobile telephone subscribers, and cable

television subscribers.

Table 2-3. Increase in ICT Diffusion in the 7 Most Populous Countries Increases, 1999-2002 (in millions) Cellular 2002 Internet Personal Fixed Telephone Country Mobile Population Users Computers Line Receivers Subs 1,285 China 50 10 106 163 40 1,042 India 14 3 15 11 10 288 U.S. 53 37 6 55 34 212 Indonesia 7 0 2 9 2 174 Brazil 11 7 14 20 4 149 Pakistan 1 0 1 1 5 147 Russia 5 8 5 16 5 Source: World Economic Forum, 2004

In Table 2-3, we can see that many of the most populous countries in the

developing world, such as Brazil and Russia, also posted the largest increases over the

year.

Generally, most of these countries are developing countries except for the US, and

they have a great number of people who are potentially still a market for technologies.

Although universal access for individuals in these countries is an unlikely goal, these

countries show great potential for further growth. Based on these findings, and

controlling for country and year, I hypothesized the following:

• H19: The higher a country's population, the higher its rate of cellular mobile phone penetration.

Research Questions

This study addressed the factors that may affect cellular mobile phone diffusion during given years and suggested some related hypotheses controlling for country and year. Now, to verify whether the relationship between cellular mobile phone diffusion

58 and each independent variable is affected by time or not, I propose the following research question:

• RQ 1: When country and time are not controlled factors, are there differences in independent variables that may affect cellular mobile phone diffusion?

This study is interested in the diffusion of cellular mobile phones and the factors affecting diffusion from 1996 to 2002. To investigate the main factors by each year, this study proposes the following research question:

• RQ 2: What kind of independent variables are significant for each year and how do they change over time?

In addition, I reviewed the concept of global digital divide for ICTs including cellular mobile phones by examining global adoption and diffusion patterns. Recently, the “digital divide” gap has decreased in several countries. However, this is not a common situation and there are many obstacles to the diffusion of ICTs in developing countries. This study intends to investigate whether the digital divide has been reduced or increased between developing and developed countries during given years. Therefore, this study suggests the following research question:

• RQ 3: Is there a global digital divide between developed and developing countries in global cellular mobile phone diffusion? Are there any particular patterns with respect to the adoption and diffusion of cellular mobile phones?

Figure 2-7 suggests the theoretical framework based on pertinent literature.

Economic International Freedom Business/Trade Openness

Economic Development Culture- Mobile related Factor Penetration at the Country Level Market Size Consumer- related Factor

Technology Political Infrastructure Environment

Figure 2-7. Theoretical Framework

CHAPTER 3 METHODOLOGY

This study investigates the factors affecting cellular mobile phone penetration at the

country level. A total of 19 hypotheses are proposed to verify the relationship between

various factors and cellular mobile phone diffusion, controlling for country and time

factors. Furthermore, three research questions are suggested to examine the effect of

time-series on the mobile phone diffusion, the factors proposed, and the presence of a

digital divide between developed and developing countries

This chapter is organized as follows. First, the adopted research method is

explained. Second, the sampling procedure and data colleting process are presented.

Then, dependent and independent variables are introduced and operational definitions

and measurements for each dependent and independent variable follow. Lastly, the

statistical procedure is discussed.

Research Method

Secondary Analysis

The author adopts a secondary analysis for this study. Secondary analysis is used to

describe various analytical practices that use pre-existing data either to investigate new

research questions or to re-examine primary study questions for purposes of corroboration. Becker (1981, p.240) defines secondary analysis as “the reuse of social science data after they have been put aside by the researcher who gathered them. The reuse of the data can be by the original researcher or someone uninvolved in any way in

60 61

the initial research project. The research questions examined in the secondary analysis

can be related to the original research endeavor or quite distinct from it.”

Secondary analysis can provide major advantages to researchers such as saving

time, money and personnel, and it also makes it possible for researchers to understand

long-term change (Wimmer & Dominick, 2004). There are disadvantages, however. First of all, hypotheses or research questions that can be investigated are limited because the

data already exist. It is possible that the questions of interest have already been asked,

and because there is no way to go back for more information, researchers must keep their

analysis within the boundaries of the data originally collected (Babbie, 1998; Wimmer &

Domminick, 2004). In addition, there is a problem related to the recurrent question of

validity (Babbie, 1998). It is possible that the data were poorly collected, inaccurate,

fabricated, or flawed. This drawback may seriously affect a secondary analysis.

Considering this problem, this study tried to collect data from reliable sources such as

UN, World Bank, or ITU.

Despite the criticism of using secondary analysis, secondary analysis is the most

reasonable research approach for this study because secondary analysis is the most logical tool for collecting and analyzing longitudinal cross-country data without the limitations of time, money, and personnel.

Sample Selection and Data Sources

Sample Selection

In order to test the proposed hypotheses, this study examines the data from 103

countries in the world with individual countries as the unit of analysis. Note that although

this study uses substantial data from 103 countries, certain countries’ data are missing.

Therefore, for certain datasets, the author was only able to analyze data from 54

countries. For example, Hofstede’s index (1997) for culture-related factor and Landline telephone calls cost dataset provide data for fewer than 60 of the countries.

Data Source

This study is primarily a secondary analysis of existing data, which were obtained from a variety of private and public sources. Data including GDP per capita, FDI, Gini

Index, international business/trade openness, and demographic data were obtained from

the World Bank’s WDI database (World Development Indicator, www.worldbank.org).

Economic freedom index data were obtained form the Heritage Foundation

(www.heritage.org). Technology factors were obtained from ITU (www.itu.int), United

Nations Statistics Division (UNSD, http://unstats.un.org/unsd) and World Bank. A

Hofstede’ scale for cultural factors was gained from the reports by Hofstede (1997; www.geert-hofstede.com). Political factors came from the Freedom House

(www.wipo.int) and Henisz (2000). HDI-related data came from United Nations

Development Program (UNDP, http://hdr.undp.org/statistics).

Variable Operationalization and Measurement

All dependent and independent variables data were collected over a seven year

(1996-2002) period. The period was selected because this period includes numerous and

almost complete data for both dependent and independent variables, and therefore it is

possible to analyze the effects of all independent variables to the cellular mobile phone

diffusion without the problem of missing data.

Dependent Variable

The dependent variable in this study is the mobile penetration rate from UNSD

during the period of 1996-2002. Specifically, this study uses the number of cellular

mobile subscribers per 100 inhabitants as the measurement. Assuming that the extent to

which a technology is developed can be assessed by its diffusion (Lee & Chan-Olmsted,

2003), the number of cellular mobile subscribers per 100 inhabitants can be a good

indicator for the growth of cellular mobile telecommunication at the country level. For

this study, 103 countries’ cellular mobile phone penetration rates for the 7-year period were selected and analyzed.

Independent Variables and Measurement

Economic development

GDP per capita was chosen to test the effect of income while the Gini index was selected to test the effect of income inequality. GDP per capita explains the relative wealth of a country (Madden & Savage, 1998). The Gini index measures the extent to which the distribution of income among individuals or households within country deviates from a perfectly equal distribution. The Gini index is the Gini coefficient expressed in percentage form between 0 and 1, where 0 corresponds with perfect equality

(where everyone has the same income) and 1 corresponds with perfect inequality (where one person has all the income, and everyone else has zero income), and is equal to the

Gini coefficient multiplied by 100. Therefore, as the Gini index increases, income inequality also increases. The Gini indices are difficult to collect, represent measures for varying years, and the data source acknowledges the questionable quality of some of the

figures (Deininger & Squire, 1996). However, the Gini index is the only available source

of income inequality (Hargittai, 1999). The quality of the data must be kept in mind when interpreting the effects of this factor. Lower scores denote lower inequality. FDI is the last one in economic factors that can explain the attractiveness of a certain country’s market in the world market.

Technology infrastructure variables

Technology infrastructure factors were assessed by a number of communication technologies and subscribers such as fixed telephones, personal computers, television sets

and the Internet. Landline telephone call cost data are also used for examining the

substitution effect between fixed line and cellular mobile phone diffusion.

The measure of Digital Access Index from the ITU was also used here. To measure

the overall ability of individuals to access and use ICT, DAI combines eight variables,

covering five areas such as availability of infrastructure, affordability of access,

educational level, quality of ICT services, and Internet usage for 178 economies. The

results of this index point to potential obstacles in ICT adoption and can help countries

identify their relative strengths and weaknesses (ITU, 2003b). DAI values have a scale of

0 to 1 where 1 is highest access. DAI values are shown to hundreds of a decimal point.

Countries with the same DAI value are ranked by thousands of a decimal point.

Economic freedom

Economic freedom indices were obtained from the Heritage Foundation. This index

is an average score of 10 indices (trade policy, fiscal burden of government, government

intervention in the economy, monetary policy, capital flows and foreign investment, banking and finance, wages and prices, property rights, regulation, and Informal market activity) measured on a one-to-five scale, with 5 indicating the lowest level of economic freedom. At least in cross-sectional analyses, greater (lower index) economic freedom is expected to be associated with higher GDP, higher level of education or literacy rates, and stronger ICT indicators.

International business/trade openness

Openness to international trade was measured as the ratio of the sum of exports and

imports to GDP (Baliamoune-Lutz, 2003). This study used the trade to GDP data (%)

from World Bank of 103 countries. This data is the sum of exports and imports of goods and services measured as a share of GDP. The higher level of openness often explains the higher level of ICT diffusion.

Consumer-related variables

Three factors were used to explain the consumer factor. For age and urbanization, this study used the indicator from World Bank of country population by age and urbanization as measured by the proportions of different age groups and urban area. To measure education level and adult literacy level, this study used the education index from

UNDP which is based on the adult literacy rate and the combined primary, secondary, and tertiary gross enrollment ratio. This index indicates the overall educational level in a country and ranges from 0 to 1. As the scale increases, the level of education increases.

Political environment

Two factors were used to explain the degree of political constraint and political freedom. The political constraint index (Henisz, 2000) ranges from zero (no constraints) to one (maximum constraints). The index assumes that the democratic countries tend to impose more constraint (higher index).

Political freedom consists of two factors; political rights and civil rights. These indexes are measured on a one to seven scale (1-7), with 7 indicating the lowest degree of freedom. Therefore, as the freedom index increases, we can expect a lower degree of ICT diffusion.

Cultural-related variables

In order to measure the cultural differences among countries, this study used two dimensions of Hofstede’s scale; Individualism (IDV) and uncertainty avoidance index.

Individualism focuses on the degree to which the society reinforces individual or collective achievement and interpersonal relationships. A country’s lower level of individualism index explains that this country has a high-context culture. As mentioned above in the literature review, in a high-context culture, ICTs spread rapidly because people in this culture want to maintain an image that is similar to the group. Uncertainty avoidance index focuses on the level of tolerance for uncertainty and ambiguity within the society. A high uncertainty avoidance ranking indicates that the country has a low tolerance for uncertainty and ambiguity. Therefore, a higher uncertainty index will signify a higher level of possibility of mobile diffusion. Table 3-1 summarizes variables of this study.

Table 3-1. List of Variables and Data Sources (1996-2002) Variables Factors / Operational Definition Source Dependent Variable Mobile phone subscribers per 100 UNSD inhabitants Economic GDP per capita (US$) World Bank Development FDI (inflow, % of GDP) World Bank Gini index (1-100)* World Bank and UNDP International The ratio of trade to GDP (%) World Bank Business/Trade Openness Economic Freedom Economic Freedom index (1-5)* Heritage Foundation Technology Personal computers per 100 UNSD Infrastructure inhabitants Fixed telephone lines per 100 UNSD inhabitants Land line telephone call cost World Bank (US$ per 3 minuets) DAI (0-1) ITU

Table 3-1 Continued Variables Factors / Operational Definition Source Internet users per 100 inhabitants ITU Television set per 100 inhabitants World Bank Consumer-related Population age 0-14, 15-64, and World Bank Variable over 65 Education index (0-1) UNDP Urban population (% of total) World Bank Political Environment Political Constraint (0-1), Political Henisz (2000), Freedom* (Political Rights and Civil Freedom House Liberties, 1-7) Culture-related Hofstede’s Individualism (IDV)* and Hofstede (1997) Variable Uncertainty Avoidance index (UAI) Market Size Population, total World Bank * The lower the level of these factors, the higher level of mobile phone diffusion.

Digital divide measurement

Kauffman and Techatassanasoontorn (2005) studied the issue of global digital

divide for digital mobile phones by examining the global adoption and diffusion patterns.

They assumed that digital divide in mobile phones has two dimensions: subscriber

penetration gaps which indicate the disparity in the number of subscribers, and technological gaps which indicate the difference in the generations (2G, 2.5G, and 3G) of digital mobile phones available. To assess subscriber penetration gap, they used 43 countries’ annual subscriber data to compare ‘regional’ (America, Asia-Pacific, Europe,

Middle East, and Africa) and ‘country’ digital mobile phone diffusion. To assess

technical gap, they compared the various generations of digital mobile phone

technologies (2G, 2,5G, and 3G) and standards launched in the countries.

Patterned after the Kauffman and Techatassanasoontorn (2005) study, this study

assesses subscriber penetration gaps between developed and developing countries using

both descriptive data analysis and empirical data analysis on the annual subscription data.

Usually, the concept of digital divide is analyzed by using ratios of average per capita

68 penetrations of hardware in developed and developing countries (UN, 2004). Therefore, this study used the data representing mobile phone subscribers per 100 inhabitants for analyzing the digital divide between developed and developing countries.

The author further classified the 103 countries into different regions’ developed and developing countries for the analysis (Table 3-2).

Table 3-2. Developed and Developing Countries in Selected Samples 23 Developed Countries 54 Developing Countries Asia Pacific Region: Cambodia, China, Indonesia, Lao PDR, Malaysia, Mongolia, Papua New Guinea, Philippines, Thailand, Vietnam

Asia Pacific Region: Europe and Central Asia Region: Australia, New Zealand, Japan, Albania, Armenia, Bulgaria, Croatia, South Korea1. Czech Rep., Estonia, Hungary, Latvia, Lithuania, Moldova, Poland, Romania, European Region: Russia, Slovakia, Turkey, Ukraine Austria, Belgium, Denmark, Finland, France, Germany, Latin America and the Caribbean Greece, Ireland, Italy, Netherlands, Region; Portugal, Spain, Sweden, United Kingdom, Argentina, Bolivia, Brazil, Chile, Norway, Switzerland Colombia, Costa Rica, Ecuador, Guatemala, Guyana, Honduras, Jamaica, Middle East Region: Israel Mexico, Nicaragua, Panama, Paraguay, Peru, Trinidad and Tobago, Uruguay, North America Region: Canada, Venezuela United States Middle East and North Africa Region: Algeria, Egypt, Iran, Jordan, Morocco, Tunisia, Yemen

South Asia Region: India, Pakistan * Based on World Bank (www.worldbank.org) and Wikipedia (http://en.wikipedia.org)

1 South Korea, another relatively newly industrialized country, does not consider itself as developed. This has led to accusations that it prefers to avoid the obligations (Wikipedia, http://en.wikipedia.org, 2005). This study, in consideration of South Korea’s high ICT penetration, treats South Korea as developed country.

Statistical Procedures

This study deals with a panel data set that consist of 7-year observations of 103 countries. Statistically, two regression analyses: a panel regression and an ordinary least square (OLS) regression will be performed.

To test the nineteen hypotheses, this study will conduct panel regression based on generalized least square (GLS) estimation. Using panel regression, we can examine the effect of independent variables such as GDP per capita (H1), FDI (H2), Gini (H3), economic freedom (H4), economic openness (H5), fixed line telephone penetration (H6),

PC penetration (H7), Internet penetration (H8), television sets penetration (H9), landline telephone calls cost (H10), digital access index (DAI, H11), ages (H12), urban population

(H13), education (H14), political constraint (H15), political rights (H16), civil rights

(H16), individualism (H17), uncertainty avoidance (H18), and total population (Market size, H19) to the dependent variable (cellular mobile phone penetration rate) while controlling for the country and year factors. The TSCSREG (Time Series Cross Section

Regression) procedure of SAS software analyzes panel datasets that consist of multiple time-series observations on each cross-sectional unit. Such models can be patterned as follows:

where N is the number of cross sections, T is the length of the time series for each cross

section, andｐis the number of independent variables.

There are two commonly used approaches for panel data: one and two-way fixed-effect models and random-effects models (SAS/ETS User’s Guide, 1999). The

70 fixed-effect model, however, does not allow for time-invariant variables. Therefore, the random-effect model will be adopted for this study, because some factors such as Gini and DAI are time-invariant variables for which the fixed model is not available. In addition, if the specification depends only on the cross section to which the observation belongs, such a model is referred to as a model with one-way effect. A specification that depends on both the cross section and the time-series to which the observation belongs is called a model with two-way effects. In this study, the author uses the two-way effect model to verify the influence of country (cross section) and time-series on cellular mobile phone diffusion. First, the author will perform a model with two-way panel regression to identify a specification that depends on both the cross section and the time-series observations. Next, to compare with panel regression specifications, the author will perform the OLS regression which is not controlled by the country and time factors (RQ

1: When country and time are not controlled factors, are there differences in independent variables that may affect cellular mobile phone diffusion?). According to this comparison between panel regression and OLS regression, we can assess whether the time factor influences the mobile phone diffusion. The assumptions of OLS regression will be checked, and the author will test all independent variables to determine whether multicollinearity is a problem using the collinearity diagnostics procedure in SPSS statistics software, because multicollinearity in random-effects models can increase the variance of the estimated parameters and makes the estimates less precise and less powerful in hypothesis testing.

If the results of the above comparison between two regressions are different, the author will examine what kind of independent variable is significant in each of the given

years through the use of additional OLS regression (RQ 2: What kind of independent

variables are significant for each year and how do they change over time?). If such an occasion arises, OLS regression will be conducted for each of the 7 individual years and the author will compare each OLS regression result. Through this procedure, we can identify the significant variables affecting mobile phone diffusion in each year and see the changes of variables that affect mobile phone diffusion for the 7-year period.

In addition, the author will use the annual subscriber data to compare the developed and developing countries’ cellular mobile phone diffusion and will look at the subscription process across diffusion years to assess development. Then, the author will compare mean values of the mobile subscriber number per 100 inhabitants from 1996 to

2002 in both developed and developing countries to examine the possible presence of digital divide (RQ 3: Is there a global digital divide between developed and developing countries in global cellular mobile phone diffusion? Are there any particular patterns with respect to the adoption and diffusion of cellular mobile phones?). For this, t-test statistics will be used. T-tests assess whether the means of two groups are statistically different from each other. Therefore, the author will calculate the mean value of mobile subscriber growth rates for the 7-year for period the each country, and compare these mean values.

If the t-test value is significant, it can be concluded that the variables are interdependent or related. In other words, we can suggest that a digital divide between the developed and developing countries does exist.

CHAPTER 4 RESULTS

Chapter 4 consists of six parts. First, a description of the collected data is presented.

Second, the results of a correlation analysis between mobile penetration rate and its factors are discussed. Third, the hypotheses are tested, and the results of the hypothesis tests, along with the research questions are reported. Several statistical results are also provided. The Time Series Cross Section Regression (TSCSREG) is performed to examine factors affecting cellular mobile phone diffusion. The result of OLS regression

is explained successively to compare the results of panel regression. To find what kind of

independent variable is significant in each given years, OLS regression is conducted for each of the 7 individual years. Finally, in connection with the concept of digital divide, the results of additional descriptive statistics and t-test statistics are provided.

Descriptive Statistics

As indicated, this study uses 7-year cross section and time-series data from the 103 countries, obtained from various sources.

Overall Cellular Mobile Phone Penetration in 103 Countries

The cellular mobile phone penetration of the 103 countries had increased

remarkably during the period of 1996-2002. Figure 4-1 shows the average values of

cellular mobile phone subscribers per 100 people from 1996 to 2002. While penetration

rate was only 3.89 per 100 inhabitants in 1996, by the end of 2002, it reached 31.24 per

100 inhabitants.

72 73

50 40 31.24 26.58 30 20.88 20 8.9 13.99 5.92 per 100 peoples per 10 3.89 0 1996 1997 1998 1999 2000 2001 2002

Figure 4-1. Cellular Mobile Phone Subscribers in the World (1996-2002)

According to Table 4-1, Israel (95.5 per 100 inhabitants) was the top ranked in cellular mobile phone subscribers per 100 inhabitants in 2002. Hong Kong (94.3), Italy

(93.9), Sweden (88.9), Finland (86.7), Czech Republic (84.9), Greece (84.5), Norway

(84.4), United Kingdom (84.1), and Slovenia (83.5) followed after. In comparison to the

1996 ranking, Nordic countries, (Sweden, Norway (84.4), and Finland (86.7)) were still on the list of the top countries, but the number of mobile subscribers in some East Asia and Eastern European countries had also remarkably increased. On the other hand, low-ranking countries in 1996 such as Niger (0.1), Nepal (0.1), and Mali (0.5) continued the trend into 2002. Furthermore, this result showed an increasing gap in the number of subscribers between these low-ranking countries and high-ranking countries both within year and between years. In 1996, Finland had the highest level of mobile phone subscribers, and the difference between Finland and low-ranking countries was 29.3%. In

2002, Israel had the highest level of mobile phone subscribers and Niger had the lowest level of mobile phone subscribers, and the difference between these two countries was no less than 95.4%. Ostensibly, the cellular mobile phone diffusion gap between the high and low-ranking countries had widened. Table 4-1 shows most of the low-ranking countries in 1996 and 2002 were African countries. Niger and Ethiopia, the

lowest-ranking countries added only 0.1% to their mobile phone subscribers throughout

the 7-year period.

Table 4-1. Top and Low Ranked Countries in Cellular Mobile Subscribers per 100 Inhabitants 2002 High-Rank 2002 Low-Rank 1996 High-Rank 1996 Low-Rank Israel 95.5 Niger 0.1 Finland 29.3 India, Hong Kong 94.3 Nepal 0.1 Norway 28.7 Ethiopia, Italy 93.9 Ethiopia 0.1 Sweden 28.2 Kenya, Papua New Madagascar, Sweden 88.9 0.3 Denmark 25.1 Guinea Malawi, Finland 86.7 Mali 0.5 Australia 21.8 Mali, Czech Mauritania, 84.9 Malawi 0.8 Japan 21.4 Republic Moldova, Greece 84.5 Burkina Faso 0.8 Hong Kong 21.2 Mongolia, 0.0 Norway 84.4 Bangladesh 0.8 Israel 18.2 Mozambique, United United Nepal, Niger, 84.1 Pakistan 0.9 16.4 Kingdom States Nigeria, Senegal, Tanzania, New Slovenia 83.5 Madagascar 1.0 13.4 Uganda, Zealand Zambia, Zimbabwe,

Table 4-2 shows the mobile subscriber changes between 1996 and 2002 for the

countries examined. The regions of Asia, Africa, the Middle East, Latin America, North

America and Oceania countries all had diverse subscription levels. Among these

countries, Italy (82.7%) had the highest change in the level of cellular mobile phone

subscription. Slovenia (81.4%), Greece (79.4%), Israel (77.3%), and Spain (74.8%) also

incurred large changes for the period. Overall, Europe had homogeneously increased

cellular mobile phone subscribers and some Asia-Pacific countries such as Japan

(42.2%), South Korea (60.9%), Hong Kong (73.1%) and Singapore (67.9%) also did.

Obviously, some Oceania countries such as Australia (42.2%) and New Zealand (48.8%),

as well as the United States (32.4%) and Chile (40.6%) also experienced a relatively high

level of mobile phone subscriber changes.

Note that there are two unique countries in the mobile phone market regarding the aspect of market size. China’s mobile subscribers increased only 15.5% throughout the 7 years but because of China’s sizeable population, China occupies the largest mobile phone market in the world. Similarly, although the increase rate was just 1.2% in India from 1996 to 2002, India is also becoming a major market due to their large population.

In comparison to the European and some East Asian countries, nations in Latin

America, Africa, and the Middle East showed a relatively smaller increase of cellular mobile phone subscribers. In Latin America, Chile (40.6%), Paraguay (28.1%) and

Venezuela (23%) had garnered a moderate rate of subscription, with Uruguay (16.8%),

Argentina (16.2%), Panama (18.6%), and Brazil (18.5%) following close behind. There is a small number of Middle East countries in this study because of the lack of data for many major variables. Only Jordan (increased 22.5%), Iran (increased 3.3%), and Yemen

(increased 2%) were selected. On the surface, these countries showed a relatively low level of mobile phone penetration rate. Lastly, Table 4-2 indicates that most of the

African countries had the lowest increase in mobile phone subscription. Only Botswana

(24.1%), Morocco (20.7%), and Egypt (6.7%) showed a noticeable increase in subscribers while the rest of the African countries’ mobile subscribers increased less than

5% from 1996 to 2002.

Table 4-2. Comparison of Cellular Mobile Phone Subscriber Growth in 103 Countries Country 1996 2002 % Country 1996 2002 % Albania 0.1 27.6 27.5 Lithuania 1.4 47.5 46.1 Algeria 0 1.3 1.3 Madagascar 0 1 1 Argentina 1.6 17.8 16.2 Malawi 0 0.8 0.8 Armenia 0 1.9 1.9 Malaysia 7.2 37.7 30.5 Australia 21.8 64 42.2 Mali 0 0.5 0.5 Austria 7.4 78.6 71.2 Mauritania 0 9.2 9.2 Bangladesh 0 0.8 0.8 Mexico 1.1 25.4 24.3 Belgium 4.7 78.6 73.9 Moldova 0 7.7 7.7 Bolivia 0.4 10.5 10.1 Mongolia 0 8.9 8.9 Botswana 0 24.1 24.1 Morocco 0.2 20.9 20.7

Table 4-2 Continued Country 1996 2002 % Country 1996 2002 % Brazil 1.6 20.1 18.5 Mozambique 0 1.4 1.4 Bulgaria 0.3 33.3 33 Nepal 0 0.1 0.1 Burkina Faso 0 0.8 0.8 Netherlands 6.5 74.5 68 Cambodia 0.2 2.8 2.6 New Zealand 13.4 62.2 48.8 Cameroon 0 4.3 4.3 Nicaragua 0.1 3.8 3.7 Canada 12.1 37.7 25.6 Niger 0 0.1 0.1 Chile 2.2 42.8 40.6 Nigeria 0 1.3 1.3 China 0.6 16.1 15.5 Norway 28.7 84.4 55.7 Colombia 1.3 10.6 9.3 Pakistan 0.1 0.9 0.8 Costa Rica 1.4 11.1 9.7 Panama 0.3 18.9 18.6 Papua New Croatia 1.5 53.5 52 0.1 0.3 0.2 Guinea Czech Republic 1.9 84.9 83 Paraguay 0.7 28.8 28.1 Denmark 25.1 83.3 58.2 Peru 0.8 8.6 7.8 Ecuador 0.5 12.1 11.6 Philippines 1.4 19.1 17.7 Egypt 0 6.7 6.7 Poland 0.6 36.3 35.7 Estonia 4.7 65 60.3 Portugal 6.7 82.5 75.8 Ethiopia 0 0.1 0.1 Romania 0.1 23.6 23.5 Russian Finland 29.3 86.7 57.4 0.2 12 11.8 Federation France 4.2 64.7 60.5 Senegal 0 5.5 5.5 Gambia 0.3 7.5 7.2 Singapore 11.7 79.6 67.9 Germany 6.7 72.8 66.1 Slovakia 0.5 54.4 53.9 Ghana 0.1 2.1 2 Slovenia 2.1 83.5 81.4 Greece 5.1 84.5 79.4 Spain 7.6 82.4 74.8 Guatemala 0.4 13.2 12.8 Sri Lanka 0.4 4.9 4.5 Guinea 0 1.2 1.2 Sweden 28.2 88.9 60.7 Guyana 0.1 9.9 9.8 Switzerland 9.4 78.9 69.5 Honduras 0 4.9 4.9 Tanzania 0 2.2 2.2 Hong Kong, 21.2 94.3 73.1 Thailand 3.2 26 22.8 Trinidad and Hungary 4.6 67.6 63 0.8 27.8 27 Tobago India 0 1.2 1.2 Tunisia 0.1 5.9 5.8 Indonesia 0.3 5.5 5.2 Turkey 1.3 34.8 33.5 Iran 0.1 3.4 3.3 Uganda 0 1.6 1.6 Ireland 8 76.3 68.3 Ukraine 0.1 8.4 8.3 Israel 18.2 95.5 77.3 United Kingdom 12.3 84.1 71.8 Italy 11.2 93.9 82.7 United States 16.4 48.8 32.4 Jamaica 2.2 53.3 51.1 Uruguay 2.5 19.3 16.8 Japan 21.4 63.6 42.2 Venezuela 2.6 25.6 23 Jordan 0.4 22.9 22.5 Viet Nam 0.1 2.3 2.2 Kenya 0 3.8 3.8 Yemen 0.1 2.1 2 Korea (ROK) 7.1 68 60.9 Zambia 0 1.3 1.3 Lao PDR 0.1 1 0.9 Zimbabwe 0 3 3 Latvia 1.1 39.4 38.3

Review of the Suggested Independent Variables

Table 4-3 presents the descriptive statistics for all the independent variables. The average GDP per capita was $7,363.81 in 1996, and increased to $7,316.62 in 2002. On

77 average, GDP per capita from 1996 to 2002 is $7,127.09. The average FDI (% of GDP) was 3.38 from 1996-2002, but showed a high standard deviation (SD=5.67). International business/trade openness increased slightly from 1996 to 2002.

The degrees of the economic and political freedom (political rights and civil liberties) for each of the countries improved slightly between 1996 and 2002. In 2002, these indexes were reduced 0.17 (economic freedom), 0.11 (political rights), and 0.37

(civil liberties) points respectively as compared with the values from 1996. The lower levels of economic freedom index indicate the higher levels of economic freedom.

Similarly, the lower levels of political rights index and civil liberties index were related to a higher level of political freedom. Therefore, the circumstances of economic and political environment had become advantageous for mobile phone diffusion.

From 1996 to 2002, political constraint was reduced 0.06%. Political constraint assumes that the democratic countries tend to impose more constraint. Therefore, during these years, it seems that there might have been a slight shift toward a more sudden change in government policy.

In average, 13.9 individuals among 100 inhabitants owned personal computers and

21.3 individuals among 100 inhabitants owned fixed telephone lines in 1996-2002.

However, the differences among countries were also large in terms of standard deviation

(SD=14.71 and 22.84, respectively). The average Internet users (7.67 per 100 inhabitants) and television set owners (28.13 per 100 inhabitants) also have large differences in terms of standards deviation (SD=12.53, 22.84, respectively). The cost of landline telephone calls went down 2 cents per 3 minutes for the period.

In 2002, the portion of population below 64 years old reached almost 90%, the

population over 65 years old occupied just 7.8% of the population. The population living in urban areas (51%) was almost the same as the rural population. The total average population of the 103 countries in 2002 exhibited approximately 55 million.

Other independent variables such as the Gini index, DAI value, Education index,

Individualism and Uncertainty Avoidance index are time invariant variables. The average

Gini index was 39.50, and DAI is 0.45. The average education index in the 103 countries recorded was relatively high (0.78).

Table 4-3. Descriptive Statistics of Independent Variables variables Mean 1996 2002 1996-2002 SD (96-02) GDP per capita 7,363.81 7,316.62 7,127.09 10,127.14 FDI 2.30 3.38 3.78 5.67 Gini - 39.50 39.50 9.27 Economic Freedom 3.09 2.92 3.00 0.71 Political Rights 3.03 2.92 2.98 1.96 Civil Liberties 3.44 3.07 3.29 1.56 Personal Computer 7.11 13.93 10.27 14.71 Fixed Telephone Line 19.04 22.61 21.30 21.85 DAI - 0.45 0.45 0.22 Internet User 1.79 14.75 7.67 12.53 Pop 0-14 31.62 29.53 30.57 10.88 Pop 15-64 60.81 62.34 61.59 6.49 Pop over 65 7.56 8.11 7.82 5.05 Education index - 0.78 0.78 0.19 Urban Population 55.08 57.18 56.10 23.19 Political Constraint 0.54 0.48 0.51 0.29 Total Population 50,630,636 54,512,329 52,593,887 158,895,032.7 TV sets 25.08 31.96 28.13 22.84 International 73.63 79.75 77.92 42.10 Business/Trade Openness Landline Telephone 0.09* 0.07* 0.08* 0.05* Calls Cost Individualism index - 43.98* 43.98* 24.17* Uncertainty Avoidance - 64.62* 64.62* 23.73* index * 54-country set

Correlations between Variables and Multicollinearity

Correlations between Variables

Before performing regression statistics for hypotheses testing, variables were

checked for correlations. Table 4-4 presents the correlation coefficients for the variables.

All pairs between dependent variable (cellular mobile phone subscribers per 100 inhabitants, Mobile) and each independent variable showed a significant level of correlation. Among these, some pairs showed a high level of correlation. For example,

Mobile and Internet users (IU) were highly correlated (r=.873; p < .01). Technology infrastructure variables such as personal computer (PC), fixed telephone line (FTL), DAI, and television sets (TV) were also highly correlated with Mobile at .793 (p < .01), .763 (p

< .01), .693 (p < .01), and .680 (p < .01) respectively. Mobile and GDP (r=0.700, p <

.01), economic freedom index (EF, r=-.615; p < .01), population 0-14 (POP1, r=-.612; p

< .01), and population over 65 (POP3, r=.644; p < .01) were also highly correlated.

The following were all significantly correlated with Mobile, but their relationships were weak: FDI (r=.269; p < .01), Gini index (Gini, r=-.352; p < .01), political constraint

(POLC, r=.337; p < .01), total population (TPOP, r=-.082; p < .01), international business/trade openess (EOPEN, r=.208; p < .01), landline telephone calls cost (LTCC, r=.241; p < .01), and uncertainty avoidance index (UAI, r=-.133; p < .01). Finally,

Mobile and political rights (PR, r=-.429; p < .01), civil rights (CR, r=-.507; p < .01), population 15-64 (POP2, r=.524; p < .01), education index (EDU, r=.498; p < .01), urban population (UPOP, r=.534; p < .01), and individualism (IDV, r=.500; p < .01) were moderately correlated.

Table 4-4. Correlation Matrix y x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 X20 X21

y 1 .

x1 .700(**) 1

x2 .269(**) .203(**) 1

x3 -.352(**) -.417(**) -.088(*) 1

x4 -.615(**) -.701(**) -.270(**) .197(**) 1

x5 -.429(**) -.484(**) -.124(**) .283(**) .535(**) 1

x6 -.507(**) -.585(**) -.145(**) .268(**) .625(**) .906(**) 1

x7 .793(**) .895(**) .218(**) -.389(**) -.691(**) -.463(**) -.587(**) 1

x8 .763(**) .908(**) .225(**) -.502(**) -.724(**) -.587(**) -.663(**) .885(**) 1

x9 .693(**) .791(**) .227(**) -.415(**) -.736(**) -.612(**) -.654(**) .773(**) .915(**) 1

x10 .873(**) .727(**) .239(**) -.345(**) -.622(**) -.413(**) -.532(**) .898(**) .773(**) .685(**) 1

x11 -.612(**) -.646(**) -.210(**) .573(**) .595(**) .579(**) .591(**) -.617(**) -.836(**) -.891(**) -.562(**) 1

x12 .524(**) .529(**) .211(**) -.479(**) -.556(**) -.484(**) -.486(**) .534(**) .724(**) .855(**) .487(**) -.955(**) 1

x13 .644(**) .711(**) .181(**) -.617(**) -.568(**) -.624(**) -.647(**) .640(**) .869(**) .819(**) .584(**) -.925(**) .771(**) 1

x14 .498(**) .547(**) .204(**) -.294(**) -.577(**) -.570(**) -.589(**) .536(**) .697(**) .856(**) .481(**) -.810(**) .805(**) .708(**) 1

x15 .534(**) .612(**) .262(**) -.190(**) -.639(**) -.449(**) -.489(**) .594(**) .726(**) .804(**) .528(**) -.687(**) .668(**) .620(**) .718(**) 1 80

x16 .337(**) .409(**) .106(**) -.178(**) -.471(**) -.719(**) -.693(**) .388(**) .498(**) .574(**) .341(**) -.513(**) .469(**) .500(**) .594(**) .433(**) 1

x17 -.082(*) -.055 -.079(*) -.010 .146(**) .142(**) .167(**) -.067 -.079(*) -.031 -.057 -.034 .094(*) -.049 -.055 -.130(**) -.105(**) 1

x18 .680(**) .781(**) .161(**) -.487(**) -.659(**) -.577(**) -.638(**) .771(**) .898(**) .863(**) .701(**) -.851(**) .745(**) .874(**) .726(**) .720(**) .475(**) -.003 1

x19 .208(**) .069 .405(**) -.112(**) -.233(**) -.061 -.070 .125(**) .167(**) .238(**) .154(**) -.247(**) .301(**) .147(**) .255(**) .140(**) .063 -.230(**) .085(*) 1

x20 .241(**) .472(**) .127(*) -.276(**) -.403(**) -.533(**) -.575(**) .292(**) .443(**) .420(**) .158(**) -.469(**) .287(**) .573(**) .463(**) .296(**) .391(**) -.242(**) .516(**) .003 1

x21 .500(**) .718(**) .132(*) -.622(**) -.523(**) -.573(**) -.642(**) .694(**) .769(**) .703(**) .566(**) -.674(**) .501(**) .733(**) .575(**) .456(**) .457(**) .037 .770(**) -.013 .473(**) 1

x22 -.133(**) -.226(**) -.099 .202(**) .126(*) -.242(**) -.104(*) -.378(**) -.142(**) -.096 -.275(**) -.067 -.011 .134(**) .075 .030 .236(**) -.134(**) -.035 -.301(**) .188(**) -.220(**) * p < .05; ** p < .01 a. Descriptions: x1: GDP per capita; x2: FDI; x3: Gini index; x4: Economic Freedom index; x5: Political Rights; x6: Civil Liberties; x7: Personal Computers; x8: Fixed Telephone Lines; x9: DAI; x10: Internet Users; x11: Pop 0-14; x12: Pop 15-64; x13: Pop 65; x14: Education index; x15: Urban Population; x16: Political Constraint; x17: Population, Total; x18: Television Sets; x19: International Business/Trade Openness; x20: Landline Telephone Calls Cost; x21: Individualism; x22: Uncertainty Avoidance index (UAI) b. x1- x19 from 103-country dataset, x20, x21 and x22 from 54-country dataset

Multicollinearity Problems

Since some independent variables showed a high correlation between each other

(GDP and FTL (r=.908; p < .01), PR and CR (r=.906; p < .01), FTL and DAI (r=.915; p <

.01), POP1 and POP2 (r=-.955; p < .01), POP1 and POP3 (r=-.925; p < .01)), the collinearity (or multicollinearity) statistics were performed. Similar to its impact in OLS regression, multicollinearity in random-effects models can increase the variance of the estimated parameters (Kaufman & Techatassanasoontorn, 2005). This makes the estimates less precise, thus resulting in less powerful hypotheses tests.

This study checked the multicollinearity problem before running regression through the collinearity diagnostics procedure in SPSS software. In SPSS, the presence of multicollinearity is indicated by the tolerance and a variance inflation factors (VIF) analysis. The tolerance for a variable is 1 - R-squared for the regression of that variable on all the other independents, ignoring the dependent. When tolerance is close to 0, there is high multicollinearity of that variable with other independents and the b and beta coefficients will be unstable. VIF is the variance inflation factor, which is simply the reciprocal of tolerance. This is defined as 1/VIF. Therefore, when VIF is high, there is high multicollinearity and instability of the B and beta coefficients. But there are no formal criteria for deciding if a VIF is large enough to affect the predicted values (SAS

OnlineDoc8, http://v8doc.sas.com/sashtml).

Table 4-5 shows the result of the excluded variable by the collinearity statistics.

POP1 was excluded because the coefficient of tolerance was extremely close to zero

(1.66E-013) and the coefficient of VIF was too high (6.03E+012). All other independent variables passed the examination. Consequently, the author included all independent variables except POP1 in the analysis.

Table 4-5. Excluded Variable by SPSS Variables Collinearity Statistics Tolerance VIF Minimum Tolerance POP1 1.66E-013 6.03E+012 1.66E-013

Hypotheses Tests

The Two-Way Random Effect Panel Regression

This study used a two-way panel regression procedure with a control of the country and time factors to test the hypotheses. Table 4-6 shows the result of the two-way random model panel regressions. Note that the dependent variable here was cellular mobile phone subscribers per 100 inhabitants and the number of cross sections (countries) was 103. For

LTCC, IDV, and UAI, this study used another 54-country dataset. Time series length was

7 years.

First, the two-way panel regressions using two datasets reported a significant value

of R2 coefficients (0.6166 in 103 dataset, 0.5446 in 54 dataset). This provides a relatively

strong support for this regression’s specification. However, the variance component for

cross sections (1538.748) was much bigger than the variance component for time series

(22.04671). This implies the time effect to the mobile diffusion was minimal. In essence, based on the cross section and time series variance component in two-way panel regression, it was evident that the mobile phone diffusion was affected more by the factors reflected by each country’s characteristic (cross sectional characteristics).

Table 4-6. The Result of the Two-Way Random Effect Panel Regression Variable B SE GDP per capita 0.000325 0.000271 FDI 0.156272* 0.0653 Gini 0.510838 0.4752 EF -1.86136 1.0572 PR 0.358674 0.5795 CR -0.29449 0.8313 PC -0.03721 0.1466

Table 4-6. Continued Variable B SE FTL 0.545907** 0.1789 DAI -25.6774 45.7676 IU 1.204398** 0.0890 POP2 -1.58143** 0.4293 POP3 1.588105 0.8638 EDU 23.82819 37.6572 UPOP -0.4365 0.2242 POLC -0.42618 2.4909 TPOP -1.81E-8 2.198E-8 TV 0.025205** 0.00703 EOPEN 0.101982** 0.0353 R2 (103 dataset) 0.6166 LTCC 95.15914** 25.6177 IDV 0.10095 0.6799 UAI 0.370349 0.4456 R2 (54 dataset) 0.5446 *p < .05; **p < .01

Hypotheses Tests

The hypotheses proposed in this study considered the cross section effect as well as

the time-series effect. Based on the estimated coefficients in the two-way random effect

panel regression, the effects of GDP per capita (H1) and Gini (H3) were rejected while

the effect of FDI (H2, BFDI = 0.156272, p < .05) was supported. Specifically, because

BFDI was positive, the more FDI was likely to lead the higher the level of mobile phone

diffusion. Although GDP (income) showed a positive trend, it was not supported by the

regression. It also seems that Gini did not contribute to the cellular mobile phone

diffusion. In short, among the economic development variables, just one factor, FDI, was significant.

The economic freedom variable (H4) was not supported, however the economic

openness variable (H5, BEPOEN = 0.101982, p < .01) was significant. The result for the

coefficient of H5 was positive. It seems that higher levels of international business/trade

openness lead to a better level of mobile phone diffusion. Note that the coefficient of BEF

was negative, but in this case, variable 5 indicates the lowest level of economic freedom,

so this direction was reasonable. In addition, similar to GDP and Gini, economic freedom

was related to each country’s income level. Overall, income-related factors were not

supported by the regression result here.

Among the technology infrastructure variables, PC (H7) and DAI (H11) were not

related to cellular mobile phone diffusion while the rest of variables, FTL (H6, BFTL =

0.545907, p < .01), IU (H8, BIU = 1.204398, p < .01), TV (H9, BTV = 0.025205, p < .01),

and LTCC (H10, BLTCC = 95.15914, p < .01) were significant. All signs of β coefficients

were positive. That is to say, the higher level of fixed telephone line penetration, Internet

users, television sets, and landline telephone call cost, the higher its level of cellular

mobile phone penetration in a country.

POP2 (H12, age 15-64) was significant while POP3 (H12, age over 65) was not

related to mobile diffusion. However, because the direction of the coefficient was

negative (BPOP2 = -1.58143), the H12 which proposed a positive relationship was, in the end, not supported. POP3 also showed a reverse coefficient in terms of its hypothesis. In the case of UPOP (H13) and EDU (H14), these two consumer-related variables were not

related to the mobile phone diffusion. Political variables here included 3 factors; POLC

(H15), PR (H16), CR (H16). However, none were supported by the regression. This

indicated that political environment variables here were not related to the cellular mobile

phone diffusion.

IDV (H17) and UAI (H18) belonged to the culture-related variable and MS (H19)

was the factor of market size. Although the results showed a positive sign for the β

coefficients, individualism and uncertainty avoidance were not supported by the

regression. TPOP was also insignificant and did not contribute to the diffusion of mobile

phone use.

In summary, the two-way panel regression, controlling for 103 country and time

factors (7 years) shows that FDI, International Business/Trade Openness, Fixed telephone lines, Internet Users, Television Sets, and Landline telephone calls cost were the factors affecting cellular mobile phone diffusion at the country level.

Table 4-7. Summary of Hypotheses Testing (considered cross section and time-series) Hypotheses Findings Results Hypothesis 1 Insignificant, + sign Rejected Hypothesis 2 Significant, - sign. Supported Hypothesis 3 Insignificant, + sign Rejected Hypothesis 4 Insignificant, - sign Rejected Hypothesis 5 Significant, + sign Supported Hypothesis 6 Significant, + sign Supported Hypothesis 7 Insignificant, - sign Rejected Hypothesis 8 Significant, + sign Supported Hypothesis 9 Significant, + sign Supported Hypothesis 10 Significant, + sign Supported Hypothesis 11 Insignificant, - sign Rejected Hypothesis 12 Significant (POP2), - sign / Insignificant (POP3), - sign Rejected Hypothesis 13 Insignificant, - sign Rejected Hypothesis 14 Insignificant, + sign Rejected Hypothesis 15 Insignificant, - sign Rejected Hypothesis 16 Insignificant, - sign Rejected Hypothesis 17 Insignificant, + sign Rejected Hypothesis 18 Insignificant, + sign Rejected Hypothesis 19 Insignificant, - sign Rejected

Research Questions

Panel Regression versus OLS Regression

This study conducted a two-way panel regression to investigate the factors

affecting cellular mobile phone diffusion controlling for cross section (country) and time-

series. As a result of the two-way panel regression, when we controlled country and time

factors, FDI, EOPEN, FTL, IU, TV, and LTCC were significant. Now, to verify whether

the relationship between cellular mobile phone diffusion and each independent variable

was affected by the time factor, this study performed OLS regression which consists of

the data for the 103 countries over a 7 year period. Under the OLS regression, however,

the country and time factors are not controlled. Table 4-8 summarized the results of OLS regressions using two datasets. Theses OLS regressions showed highly significant R2

coefficients (0.808 in the 103 dataset, 0.821 in the 54 dataset) and also showed that each

F-statistic indicated a high level of significance.

Table 4-8. Result of the OLS Regression (not considered cross section and time-series) Variables β t-Value Sig. GDP per capita .126* 2.245 .025 FDI .026 1.317 .188 Gini .069** 2.802 .005 EF -.054 -1.849 .065 PR -.107* -2.366 .018 CR .146** 3.036 .002 PC -.239** -3.123 .002 FTL .033 .335 .738 DAI .147 1.760 .079 IU .877** 19.546 .000 POP2 -.006 -.138 .890 POP3 .289** 5.025 .000 EDU -.073 -1.785 .075 UPOP -.021 -.661 .509 POLC -.044 -1.670 .095 TPOP -.023 -1.139 .255 TV -.146** -3.091 .002 EOPEN .038 1.796 .073 R2 (103 dataset) 0.808 F-statistics 156.011 (p < .000) LTCC .084* 2.399 .017 IDV -.125* -2.363 .019 UAI -.008 -.194 .846 R2 (54 dataset) 0.821 F-statistics 70.804 (p < .000) *p < .05; **p < .01

According to Table 4-8, GDP per capita (βGDP = 0.126; p < .05) was positive and

significant. PR (βPR = -.017; p < .05) was significant in a negative direction as expected.

Both IU (βIU = 0.877; p < .01), and LTCC (βLTCC = 0.084; p < .05) were positive and

significant. IND (βIND = -0.125; p < .05) was negative and significant as expected. These variables supported the cellular mobile phone diffusion.

On the contrary, Gini (βGini = 0.069; p < .01), CR (βCR = 0.146; p < .01), TV (βTV =

-0.146; p < .01), PC βPC = -0.237; p < .01), and POP3 (βPOP3 = 0.289; p < .01) were

significant but each coefficient was opposite of what was anticipated. These variables

here were significant, but did not support the proposition.

By Comparison, GDP per capita, PR, IDV only supported the cellular mobile diffusion in the OLS regression, while FDI, FTL, TV, and EOPEN only supported the cellular mobile phone diffusion in the panel regression. IU and LTCC supported cellular

mobile phone in both panel regression and OLS regression. Table 4-9 shows the result of

a comparison between panel regression and OLS regression. Although other variables

such as POP2 in the panel regression and Gini, CL, PC, POP3, and TV in the OLS regression were also significant, these did not support cellular mobile phone diffusion

due to the reverse direction of their coefficients.

Table 4-9. Panel regression versus OLS regression Regression Panel Regression OLS regression Variable B β GDP per capita 0.000325 .126* FDI 0.156272* .026 Gini 0.510838 .069** EF -1.86136 -.054 PR 0.358674 -.107* CL -0.29449 .146** PC -0.03721 -.239** FTL 0.545907** .033 DAI -25.6774 .147

Table 4-9. Continued Regression Panel Regression OLS regression IU 1.204398** .877** POP2 -1.58143** -.006 POP3 1.588105 .289** EDU 23.82819 -.073 UPOP -0.4365 -.021 POLC -0.42618 -.044 TPOP -1.81E-8 -.023 POP3 -1.81E-8 -.023 TV 0.025205** -.146** EOPEN 0.101982** .038 LTCC 95.15914** .084* IDV 0.10095 -.125* UAI 0.370349 -.008 *p < .05; **p < .01

In other words, when we controlled country and time factors, FDI, Fixed telephone line per 100 inhabitants, TV sets per 100 inhabitants, and International business/trade positively influenced the cellular mobile phone diffusion in a country. When country and time factors were not controlled, it was clear that GDP per capita, Political rights, and

Individualism affected the cellular mobile phone diffusion in a country. In both situations, Internet users per 100 inhabitants and Landline telephone call cost were factors affecting mobile phone diffusion in a country.

In conclusion, the time effect existed but was minimal, and cross section effect was the major effect on the mobile phone diffusion. There were some differences in the role of the independent variables depending on whether we controlled the country and time factors. Next, the author examines which factors were significant during each year and how these factors changed from time to time.

Major Factors from 1996 to 2002

To find the major factors affecting cellular mobile phone diffusion in each year at the country level, the author conducted the OLS regression for each (1996-2002). Table

4-10 shows the result of OLS regressions.

Table 4-10. Results of OLS Regressions by Years (1996-2002) Year 1996 1997 1998 1999 2000 2001 2002 β GDP per .207 .594** .645** .554** .335** .261* .166 capita FDI -.012 .017 .027 -.068 -.018 .006 .061 Gini -.100 -.058 -.025 .036 .030 -.027 -.037 EF .103 .043 -.076 -.050 -.124 -.090 -.064 PR .092 .025 .035 .016 -.041 -.037 -.063 CR -.013 -.009 .037 .036 .145 .077 .008 PC .215 -.352 -.673** -.286 -.077 -.246 -.344* FTL -.010 .160 .348 .098 .110 .270 .249 DAI .603* .426 .493* .690* .751** .852** .967** IU .281* .471** .532** .294** .152 .159 .175 POP2 -.392* -.126 -.067 -.161 -.168 -.258** -.240* POP3 -.045 -.098 -.056 .258 .394** .309* .242* EDU .013 -.031 -.125 -.141 -.195* -.170* -.181* UPOP -.073 -.066 -.065 -.037 -.028 -.034 -.064 POLC .092 .015 -.021 -.026 -.007 -.020 -.080 TPOP -.008 -.027 -.021 -.020 -.024 -.019 -.031 TV .074 -.067 -.162 -.299* -.378** -.330** -.205* EOPEN -.099 -.045 .001 .047 .033 .049 .066 R2 (103 0.800 0.842 0.861 0.886 0.900 0.909 0.907 dataset) F- 13.900** 22.287** 28.971** 35.574** 41.297** 45.929** 44.375** statistics LTCC -.057 .068 -.041 .051 .119 .090 .155 IDV -.472* -.212 -.091 -.073 .032 .013 -.170 UAI -.208 -.092 -.039 -.090 -.119 -.158 -.185 R2 (54 0.879 0.851 0.853 0.892 0.929 0.944 0.949 dataset) F- 7.990** 7.896** 8.278** 11.406** 19.946** 24.244** 16.815** statistics *p < .05; **p < .01

In 1996, DAI (βDAI1996 = 0.603; p < .05), IU (βIU1996 = 0.281; p < .05), and IDV

(βIDV1996 = -0.472; p < .05) were significant. POP2 (βPOP21996 = -0.392; p < .05) was

statistically significant but had a reverse direction in its coefficient. In 1997, only two

factors, GDP per capita (βGDP1997 = 0.594; p < .01) and IU (βIU1997 = 0.471; p < .01) were

significant. In 1998, GDP per capita (βGDP1998 = 0.645; p < .01), DAI (βDAI1998 = 0.493; p

< .05) and IU (βIU1998 = 0.532; p < .01) were significant. PC (βPC1998 = -0.673; p < .01)

was also significant but in the opposite direction. The GDP per capita (βGDP1999 = 0.645; p

< .01), DAI (βDAI1999 = 0.493; p < .05) and IU (βIU1999 = 0.532; p < .01) were significant in

1999. TV was significant from this year to 2002 but opposite of the anticipated. GDP per

capita (βGDP2000 = 0.335; p < .01, βGD2001P = 0.261; p < .05) and DAI (βDAI2000 = 0.751; p <

.01, βDAI2001 = 0.852; p < .01) were the factors affecting mobile phone diffusion in 2000

and 2001. POP3 (βPOP32000 = 0.394; p < .01), EDU (βEDU32000 = -0.195; p < .05), and TV

(βTV2000 = -0.299; p < .05) in 2000, POP2 (βPOP22001 = -0.258; p < .01), POP3 (βPOP32001 =

0.309; p < .05), EDU (βEDU32001 = -0.170; p < .05), and TV (βTV2001 = -0.330; p < .01) in

2001 showed significant but reverse direction of coefficients. In 2002, only DAI (βDAI2002

= 0.967; p < .01) was significant. PC (βPC2002 = -0.344; p < .05, POP2 (βPOP22002 = -0.240;

p < .05), POP3 (βPOP32002 = 0.242; p < .05), and EDU (βEDU32002 = -0.181; p < .05) were

statistically significant but again different from the anticipated direction.

Generally, when we performed OLS regression separately by year, GDP per capita,

DAI, and IU were constantly significant and IDV in 1996 was exceptionally significant.

Some variables (FDI, Gini, EF, PR, CR, FTL, UPOP, PLOC, TPOP, EOPEN, LTCC, and

UAI) did not explain the mobile phone diffusion in each year, and others such as PC,

POP2, POP3, EDU, and TV were statistically significant but showed an opposite

direction of the proposed relationship.

Identifying Digital Divide in Cellular Mobile Phone Diffusion

This study also examined the gap of cellular mobile subscriber penetrations between developed and developing countries using both descriptive data analysis and empirical data analysis on the annual subscription data.

Descriptive Statistics Review

The author analyzed the 103 countries’ subscriber data and penetration growth rates to investigate the existence of a digital divide in cellular mobile phone diffusion between developed and developing counties. First, this study reviewed the changes of cellular mobile phone subscribers (per 100 inhabitants) from 1996 to 2002 in 23 developed and

54 developing countries. As the next step, this study compared the developed countries’ cellular mobile phone increase rates with that of the developing countries. Finally, this study conducted empirical tests using statistical software to verify the statistical significance of the difference in cellular mobile phone penetrations between developed and developing countries.

Table 4-11 and Table 4-12 present the percentage of the population who are cellular mobile phone subscribers in 23 developed and 54 developing countries during the

7 years. Overall, the average subscribers increased continuously from 1996 to 2002 in both developed and developing countries. As we have seen form Table 4-1, developed countries had relatively high cellular mobile phone subscribers up to 2002, ranging from

37.70 (per 100 inhabitants) in Canada to 95.50 in Israel. In 1996, although the average subscribers were 13.54, this number increased to 75.43 by 2002.

Table 4-11. Cellular Mobile Phone Subscribers* in Developed Countries (1996-2002) Country 1996 1997 1998 1999 2000 2001 2002 Israel 18.2 28.3 35.9 47.2 70.2 90.7 95.5 Italy 11.2 20.5 35.7 52.8 73.7 88.3 93.9 Sweden 28.2 35.8 46.4 58.3 71.8 80.5 88.9

Table 4-11. Continued Country 1996 1997 1998 1999 2000 2001 2002 Finland 29.3 42 55.1 63.4 72 80.4 86.7 Greece 5.1 8.9 19.3 36.7 56.1 75.2 84.5 Norway 28.7 38 47.4 61.3 74.8 83.1 84.4 United 12.3 15 25.1 45.7 72.7 77 84.1 Kingdom Denmark 25.1 27.4 36.4 49.5 63.1 74 83.3 Portugal 6.7 15.1 30.8 46.7 66.5 77.2 82.5 Spain 7.6 11 16.4 37.3 60.5 73.3 82.4 Switzerland 9.4 14.7 23.8 42.6 64.3 72.8 78.9 Austria 7.4 14.4 28.2 52 75.3 80.4 78.6 Belgium 4.7 9.6 17.2 31.1 54.9 74.7 78.6 Ireland 8 14.9 25.5 44.8 65 77.4 76.3 Netherlands 6.5 11 21.3 42.5 67.3 76.7 74.5 Germany 6.7 10.1 17 28.5 58.6 68.2 72.8 Korea (ROK) 7.1 15.3 30.9 51.3 58.3 62.1 68 France 4.2 10 19.2 36.6 49.3 60.5 64.7 Australia 21.8 24.6 26.3 33.4 44.7 57.4 64 Japan 21.4 30.3 37.4 44.9 52.6 58.8 63.6 New Zealand 13.4 15.3 21.2 37.2 40.8 59.9 62.2 United States 16.4 20.4 25.2 31 38.9 45.1 48.8 Canada 12.1 14.7 18.3 23.4 28.3 34.9 37.7 Mean 13.5435 19.4478 28.6957 43.4 59.987 70.8087 75.4304 Std. Deviation 8.36061 9.77022 10.63589 10.51942 12.80345 13.35698 13.88287 * Cellular mobile phone subscribers per 100 inhabitants

On the contrary, the developing countries had diverse subscriber levels. In general, developing countries fell into three tiers. The high subscription rate tier included Czech

Republic (84.90 per 100 inhabitants), Hungary (67.60), Estonia (65.00), Slovakia (54.40),

Croatia (53.50), Jamaica (53.30), Lithuania (47.50), Chile (42.80), Latvia (39.40), and

Malaysia (37.70).

Papua New Guinea (0.30 per 100 inhabitants), Pakistan (0.90), Lao PDR (1.00),

India (1.20), Algeria (1.30), Armenia (1.90), Yemen (2.10), Viet Nam (2.30), Cambodia

(2.80), Iran (3.40), Nicaragua (3.80), Honduras (4.90), Indonesia (5.50), Tunisia (5.90),

Egypt (6.70), Moldova (7.70), Ukraine (8.40), Peru (8.60), Mongolia (8.90), and Guyana

(9.90) represented the lower subscription rate tier. The rest of the developing countries,

93 such as Albania (27.60), Brazil (20.10), Uruguay (19.30), Philippines (19.10), Panama

(18.90), Argentina (17.80), China (16.10), Colombia (10.60), Bolivia (10.50), Guatemala

(13.20), Ecuador (12.10), and Russia (12.00) can be described as being in a middle

subscription rate tier. Within developing countries, the penetrations for mobile phone

subscribers varied tremendously.

Table 4-12. Cellular Mobile Phone Subscribers* in Developing Countries (1996-2002) Country 1996 1997 1998 1999 2000 2001 2002 Czech Republic 1.9 5.1 9.4 18.9 42.3 68 84.9 Hungary 4.6 6.9 10.5 16.2 30.8 49.8 67.6 Estonia 4.7 9.9 17 26.8 38.7 45.5 65 Slovakia 0.5 3.7 8.8 12.3 20.6 39.9 54.4 Croatia 1.5 2.7 4.1 6.6 23.1 40.1 53.5 Jamaica 2.2 2.6 3.1 5.6 14.2 24.4 53.3 Lithuania 1.4 4.5 7.2 9 14.2 27.7 47.5 Chile 2.2 2.8 6.5 15.1 22.4 34.2 42.8 Latvia 1.1 3.1 6.8 11.3 16.6 27.9 39.4 Malaysia 7.2 9.2 10.1 13.7 22 31.3 37.7 Poland 0.6 2.1 5 10.2 17.5 25.9 36.3 Turkey 1.3 2.6 5.5 12.6 24.7 29.5 34.8 Bulgaria 0.3 0.8 1.5 4.2 9.1 19.1 33.3 Paraguay 0.7 1.7 4.4 8.1 14.9 20.4 28.8 Trinidad 0.8 1.4 2 3 12.5 19.7 27.8 and Tobago Albania 0.1 0.1 0.2 0.4 1 12.7 27.6 Thailand 3.2 3.8 3.3 3.9 5 12.3 26 Venezuela 2.6 4.7 8.7 16 22.5 26.2 25.6 Mexico 1.1 1.8 3.5 7.9 14.2 21.7 25.4 Romania 0.1 0.9 2.9 6 11.1 17.2 23.6 Jordan 0.4 1 1.7 2.4 7.7 16.7 22.9 Morocco 0.2 0.3 0.4 1.3 8.2 16.4 20.9 Brazil 1.6 2.9 4.4 8.9 13.7 16.7 20.1 Uruguay 2.5 3.1 4.6 9.6 12.3 15.5 19.3 Philippines 1.4 1.9 2.4 3.8 8.4 15.5 19.1 Panama 0.3 0.7 3.1 8.3 14.4 16.4 18.9 Argentina 1.6 4.6 7.2 12.5 16.9 19.3 17.8 China 0.6 1.1 1.9 3.4 6.6 11 16.1 Guatemala 0.4 0.6 1 3 7.5 9.8 13.2 Ecuador 0.5 1.1 2 3.1 3.8 6.7 12.1 Russian Federation 0.2 0.3 0.5 0.9 2.2 5.3 12 Costa Rica 1.4 1.8 2.8 3.5 5.1 7.6 11.1 Colombia 1.3 3.2 4.4 4.7 5.3 7.6 10.6 Bolivia 0.4 1.5 3 5.2 7.1 9.4 10.5 Guyana 0.1 0.2 0.2 0.3 4.6 8.7 9.9 Mongolia 0 0.1 0.4 1.5 6.5 8.1 8.9 Peru 0.8 1.7 3 4 5 5.9 8.6 Ukraine 0.1 0.1 0.2 0.4 1.6 4.4 8.4 Moldova 0 0.1 0.2 0.4 3.2 5.1 7.7

Table 4-12. Continued Egypt 0 0.1 0.2 0.8 2.1 4.3 6.7 Tunisia 0.1 0.1 0.4 0.6 1.3 4 5.9 Indonesia 0.3 0.5 0.5 1.1 1.8 3.1 5.5 Honduras 0 0.2 0.6 1.2 2.5 3.6 4.9 Nicaragua 0.1 0.2 0.4 0.9 1.8 3 3.8 Iran 0.1 0.4 0.6 0.8 1.5 3.2 3.4 Cambodia 0.2 0.3 0.5 0.7 1 1.7 2.8 Viet Nam 0.1 0.2 0.3 0.4 1 1.5 2.3 Yemen 0.1 0.1 0.1 0.2 0.2 0.8 2.1 Armenia 0 0.1 0.2 0.2 0.5 0.7 1.9 Algeria 0 0.1 0.1 0.2 0.3 0.3 1.3 India 0 0.1 0.1 0.2 0.4 0.6 1.2 Lao PDR 0.1 0.1 0.1 0.2 0.2 0.6 1 Pakistan 0.1 0.1 0.2 0.2 0.3 0.6 0.9 Papua New Guinea 0.1 0.1 0.1 0.1 0.2 0.2 0.3 Mean 0.9852 1.8407 3.1167 5.4222 9.863 15.3296 21.2481 Std. Deviation 1.38896 2.22605 3.54043 5.92582 9.9217 14.53594 19.56868 *Cellular mobile phone subscribers per 100 inhabitants

Overall, the numbers seem to point to a certain degree of digital divide in cellular mobile phone subscribers between developed and developing countries. Specifically, there appeared to be a very wide gap between the South-East Asian and African counties, which are on the tail end of subscribers, and the European countries. There also existed a certain level of the digital divide within the developing countries. For instance, Czech

Republic had 84.90 subscribers per 100 inhabitants in 2002 while Papua New Guinea had

0.30 inhabitants. This difference became even larger over time. Table 4-13 and Table 4-

14 show the growth rates of cellular mobile phone subscribers in developed and developing countries by year. On average, the mobile phone subscribers in the developed countries increased about 61.89% from 1996 to 2002, compared to only a 20.26% increase in the developing countries.

Table 4-13. Cellular Mobile Phone Subscriber Growth Rates* in Developed Countries Country 96-97 97-98 98-99 99-00 00-01 01-02 96-02 Italy 9.3 15.2 17.1 20.9 14.6 5.6 82.7 Greece 3.8 10.4 17.4 19.4 19.1 9.3 79.4 Israel 10.1 7.6 11.3 23 20.5 4.8 77.3 Portugal 8.4 15.7 15.9 19.8 10.7 5.3 75.8 Spain 3.4 5.4 20.9 23.2 12.8 9.1 74.8

Table 4-13. Continued Country 96-97 97-98 98-99 99-00 00-01 01-02 96-02 Belgium 4.9 7.6 13.9 23.8 19.8 3.9 73.9 United Kingdom 2.7 10.1 20.6 27 4.3 7.1 71.8 Austria 7 13.8 23.8 23.3 5.1 -1.8 71.2 Switzerland 5.3 9.1 18.8 21.7 8.5 6.1 69.5 Ireland 6.9 10.6 19.3 20.2 12.4 -1.1 68.3 Netherlands 4.5 10.3 21.2 24.8 9.4 -2.2 68 Germany 3.4 6.9 11.5 30.1 9.6 4.6 66.1 Korea (ROK) 8.2 15.6 20.4 7 3.8 5.9 60.9 Sweden 7.6 10.6 11.9 13.5 8.7 8.4 60.7 France 5.8 9.2 17.4 12.7 11.2 4.2 60.5 Denmark 2.3 9 13.1 13.6 10.9 9.3 58.2 Finland 12.7 13.1 8.3 8.6 8.4 6.3 57.4 Norway 9.3 9.4 13.9 13.5 8.3 1.3 55.7 New Zealand 1.9 5.9 16 3.6 19.1 2.3 48.8 Australia 2.8 1.7 7.1 11.3 12.7 6.6 42.2 Japan 8.9 7.1 7.5 7.7 6.2 4.8 42.2 United States 4 4.8 5.8 7.9 6.2 3.7 32.4 Canada 2.6 3.6 5.1 4.9 6.6 2.8 25.6 Mean 5.9043 9.2478 14.7043 16.587 10.8217 4.6217 61.887 Std. Deviation 2.98625 3.7545 5.42574 7.69565 4.98388 3.28965 15.1211 * Cellular mobile phone subscriber growth rate: %

Some developed countries such as Austria (-1.80%), Ireland (-1.10%), and

Netherlands (-2.20%) had a decrease in growth rates during 2001-2002. The growth rates

in the developed countries were in fact slower after 2000. During 2001-2002, cellular

mobile phone subscribers increased at least 5.9% in developing countries, while the developed countries’ growth rate was about 4.6%. In general, the mobile gap between

developed and developing countries was still wide, and the mobile growth speed in the

low penetration countries could still be considered slow. During 1996-1997, the average

growth rate was 5.90% in the developed countries, compared to only of average of 0.86%

in the developing countries. The difference of growth rates between these two numbers

was 5.05%. However, during 1997-1998, the difference grew to be 7.97%, and it reached

41.6% in 2002. In essence, although the mobile growth rates in the developing countries

96 exceeded those rates of the developed countries during recent year, the gap of mobile phone penetration between the developed and developing countries examined have actually widened significantly.

Table 4-14. Cellular Mobile Phone Subscriber Growth Rates* in Developing Countries Country 96-97 97-98 98-99 99-00 00-01 01-02 96-02 Czech Republic 3.2 4.3 9.5 23.4 25.7 16.9 83 Hungary 2.3 3.6 5.7 14.6 19 17.8 63 Estonia 5.2 7.1 9.8 11.9 6.8 19.5 60.3 Slovakia 3.2 5.1 3.5 8.3 19.3 14.5 53.9 Croatia 1.2 1.4 2.5 16.5 17 13.4 52 Jamaica 0.4 0.5 2.5 8.6 10.2 28.9 51.1 Lithuania 3.1 2.7 1.8 5.2 13.5 19.8 46.1 Chile 0.6 3.7 8.6 7.3 11.8 8.6 40.6 Latvia 2 3.7 4.5 5.3 11.3 11.5 38.3 Poland 1.5 2.9 5.2 7.3 8.4 10.4 35.7 Turkey 1.3 2.9 7.1 12.1 4.8 5.3 33.5 Bulgaria 0.5 0.7 2.7 4.9 10 14.2 33 Malaysia 2 0.9 3.6 8.3 9.3 6.4 30.5 Paraguay 1 2.7 3.7 6.8 5.5 8.4 28.1 Albania 0 0.1 0.2 0.6 11.7 14.9 27.5 Trinidad and Tobago 0.6 0.6 1 9.5 7.2 8.1 27 Mexico 0.7 1.7 4.4 6.3 7.5 3.7 24.3 Romania 0.8 2 3.1 5.1 6.1 6.4 23.5 Venezuela 2.1 4 7.3 6.5 3.7 -0.6 23 Thailand 0.6 -0.5 0.6 1.1 7.3 13.7 22.8 Jordan 0.6 0.7 0.7 5.3 9 6.2 22.5 Morocco 0.1 0.1 0.9 6.9 8.2 4.5 20.7 Panama 0.4 2.4 5.2 6.1 2 2.5 18.6 Brazil 1.3 1.5 4.5 4.8 3 3.4 18.5 Philippines 0.5 0.5 1.4 4.6 7.1 3.6 17.7 Uruguay 0.6 1.5 5 2.7 3.2 3.8 16.8 Argentina 3 2.6 5.3 4.4 2.4 -1.5 16.2 China 0.5 0.8 1.5 3.2 4.4 5.1 15.5 Guatemala 0.2 0.4 2 4.5 2.3 3.4 12.8 Russian Federation 0.1 0.2 0.4 1.3 3.1 6.7 11.8 Ecuador 0.6 0.9 1.1 0.7 2.9 5.4 11.6 Bolivia 1.1 1.5 2.2 1.9 2.3 1.1 10.1 Guyana 0.1 0 0.1 4.3 4.1 1.2 9.8 Costa Rica 0.4 1 0.7 1.6 2.5 3.5 9.7 Colombia 1.9 1.2 0.3 0.6 2.3 3 9.3 Mongolia 0.1 0.3 1.1 5 1.6 0.8 8.9 Ukraine 0 0.1 0.2 1.2 2.8 4 8.3 Peru 0.9 1.3 1 1 0.9 2.7 7.8 Moldova 0.1 0.1 0.2 2.8 1.9 2.6 7.7 Egypt 0.1 0.1 0.6 1.3 2.2 2.4 6.7 Tunisia 0 0.3 0.2 0.7 2.7 1.9 5.8 Indonesia 0.2 0 0.6 0.7 1.3 2.4 5.2 Honduras 0.2 0.4 0.6 1.3 1.1 1.3 4.9 Nicaragua 0.1 0.2 0.5 0.9 1.2 0.8 3.7 Iran 0.3 0.2 0.2 0.7 1.7 0.2 3.3

Table 4-14. Continued Country 96-97 97-98 98-99 99-00 00-01 01-02 96-02 Cambodia 0.1 0.2 0.2 0.3 0.7 1.1 2.6 Viet Nam 0.1 0.1 0.1 0.6 0.5 0.8 2.2 Yemen 0 0 0.1 0 0.6 1.3 2 Armenia 0.1 0.1 0 0.3 0.2 1.2 1.9 Algeria 0.1 0 0.1 0.1 0 1 1.3 India 0.1 0 0.1 0.2 0.2 0.6 1.2 Lao PDR 0 0 0.1 0 0.4 0.4 0.9 Pakistan 0 0.1 0 0.1 0.3 0.3 0.8 Papua New Guinea 0 0 0 0.1 0 0.1 0.2 Mean 0.8556 1.2759 2.3056 4.4407 5.4667 5.9185 20.263 Std. Deviation 1.089 1.57862 2.64028 4.72363 5.60956 6.429 18.75831 * Cellular mobile phone subscriber growth rate: %

Table 4-15 and Table 4-16 show the average growth rate in each developed and developing country from 1996 to 2002. Overall, average mobile subscriber growth rate in developed countries was 10.31% while developing countries’ average growth rate was

3.38%.

Table 4-15. Average Growth Rates in Each Developed Country (1996-2002) Country Minimum Maximum Mean Std. Deviation Italy 5.6 20.9 13.783 5.5025 Greece 3.8 19.4 13.233 6.3607 Israel 4.8 23 12.883 7.2645 Portugal 5.3 19.8 12.633 5.4198 Spain 3.4 23.2 12.467 8.123 Belgium 3.9 23.8 12.317 8.2276 United Kingdom 2.7 27 11.967 9.7196 Austria -2 24 11.87 10.326 Switzerland 5.3 21.7 11.583 6.9231 Ireland -1.1 20.2 11.383 7.9748 Netherlands -2.2 24.8 11.333 10.1327 Germany 3.4 30.1 11.017 9.8239 Korea (ROK) 3.8 20.4 10.15 6.4323 Sweden 7.6 13.5 10.117 2.2868 France 4.2 17.4 10.083 4.8035 Denmark 2.3 13.6 9.7 4.0895 Finland 6.3 13.1 9.567 2.7156 Norway 1.3 13.9 9.283 4.5565 New Zealand 1.9 19.1 8.133 7.491 Australia 1.7 12.7 7.033 4.4017 Japan 4.8 8.9 7.033 1.4024 United States 4 8 5.4 1.566 Canada 2.6 6.6 4.267 1.5436 Growth Average (total developed countries) 10.3145

Table 4-16. Average Growth Rates in Each Developing Country (1996-2002) Country Mean SD Country Mean SD Czech Republic 13.833 9.6382 Philippines 2.95 2.6372 Hungary 10.5 7.4865 Tunisia 0.97 1.088 Russian Estonia 10.05 5.2072 1.967 2.5766 Federation Croatia 8.667 7.7433 Peru 1.3 0.7014 Jamaica 8.517 10.8136 Venezuela 3.833 2.8925 Chile 6.767 3.9943 Pakistan 0.13 0.137 Bulgaria 5.5 5.5169 Morocco 3.45 3.5921 Albania 4.58 6.83 Latvia 6.38 4.037 Jordan 3.75 3.5915 Viet Nam 0.367 0.3077 Brazil 3.083 1.4662 Mongolia 1.483 1.806 Argentina 2.7 2.344 Malaysia 5.08 3.437 China 2.583 1.9343 Panama 3.1 2.1335 Guatemala 2.133 1.6729 Slovakia 8.983 6.5716 Ecuador 1.933 1.8981 Poland 5.95 3.3851 Bolivia 1.683 0.5307 Moldova 1.283 1.2952 Guyana 1.633 2.0373 Thailand 3.8 5.595 Costa Rica 1.617 1.1856 Paraguay 4.68 2.736 Papua New Colombia 1.55 1.0368 0.03 0.052 Guinea Egypt 1.117 1.0187 Romania 3.917 2.2973 Indonesia 0.867 0.8756 Ukraine 1.38 1.664 Trinidad and Honduras 0.817 0.4792 4.5 4.1933 Tobago Iran 0.55 0.5958 Uruguay 2.8 1.5837 Cambodia 0.433 0.3882 Nicaragua 0.617 0.4262 Armenia 0.317 0.4446 Mexico 4.05 2.606 Algeria 0.217 0.3869 Lithuania 7.683 7.3112 India 0.2 0.2098 Turkey 5.583 3.7685 Lao PDR 0.15 0.197 Yemen 0.33 0.528

Empirical Test

Based on the data from Table 4-15 and Table 4-16, this study compared the average growth rate in developed countries with its rate in developing countries to verify whether

the gap between these countries was statistically significant. This study performed a t-test which is the most elementary method for comparing mean scores from two groups. One of the more commonly used forms of the t-test is the test for independent groups or means. The independent t-test is used when we wish to compare the statistical

significance of a possible difference between the means of two independent groups and

two groups whose scores are not related to one another (Wimmer & Dominick, 2004).

Table 4-17 and Table 4-18 illustrate the result of independent t-test showing means, standard deviations, t-value, and degrees of freedom. The average growth rate of developed countries (M = 10.3156) is higher than that of developing countries (M =

3.3767).

Table 4-17. Descriptive Statistics of Independent Sample T-test Country N Mean SD Developed 23 10.3156 2.52025 Developing 54 3.3767 3.12639

According to the Levene’s test (F = 1.043; Sig. = 0.310), this study used equal

variance assumed statistics. The result indicates that when the df (degree of freedom) was

75, t value was 9.409 (two-tailed sig. = 0.00). Therefore, it was affirmed that there was a

significant mean difference in the cellular mobile phone diffusion between developed and

developing countries during the 7-year period.

Table 4-18. The Result of Independent Sample T-test Equal Variance Assumption Levene’s Test T df Sig Equal variance assumed Sig. = 9.409 75 0.000 F = 1.043 Equal variance not assumed 0.310 10.261 51.166 0.000

In short, based on descriptive statistics and an empirical t-test, the digital divide in

mobile phone penetration still existed between the developed and developing countries

and this gap had been widened from 1996 to 2002.

CHAPTER 5 DISCUSSION AND CONCLUSION

Study Overview

This thesis aimed to provide an overview of the country-level development of

cellular mobile phone technology and to propose a comprehensive framework for mobile

diffusion by integrating a number of factors that might affect cellular mobile phone

diffusion. In addition, this study investigated the issue of a digital divide with respect to

mobile diffusion between developed and developing countries in a growing global

economy, which is driven by greater integration of world markets and drastic

developments in the environment.

This study first reviewed prior studies and research frameworks associated with

technology diffusion. Specifically, the author summarized the process of cellular mobile

phone technology and market development. The study continued with a review of the

pertinent studies and theoretical background of technology diffusion. The author also suggested a variety of country variables that could be related to the diffusion of mobile phone technology and proposed an integrated framework. The variable groups included:

1) economic development, 2) economic freedom, 3) international business/trade openness, 4) technology infrastructure, 5) consumer-related factors, 6) political environment, 7) culture-related factors and 8) market size. Finally, specific research hypotheses and research questions were proposed.

The author adopted a secondary analysis for this thesis using existing data, which were obtained from a variety of private and public sources. The 7-year period of cross

100 101

section and time-series data in the 103 countries were used. Because this study used panel

data including 103 countries over a 7-year period, the author controlled the variance of

the “year” and “country” factors to examine the effect of time-series by country on

mobile phone diffusion. A two-way random effect regression analysis was performed for

this. To investigate whether the time factor influenced mobile phone diffusion, an

additional OLS regression was performed without controlling for the country and time

factors. A further OLS regression was performed to examine what kinds of independent

variables were significant in each given year. Finally, to determine the presence of a

digital divide, the author compared mean values of mobile subscriber numbers per 100

inhabitants from 1996 to 2002 in both developed and developing countries. For this, t-test statistics were used.

Conclusions and Suggestions

Descriptive Statistics and Correlations

Descriptive statistics showed that the cellular mobile phone penetration in the 103

countries increased remarkably between 1996 and 2002. Whereas the average number of

cellular mobile phone subscribers was only 3.89 per 100 inhabitants in 1996, it reached

31.24 per 100 inhabitants in 2002. Overall, the cellular mobile phone subscription level

was extremely diverse according to regions. Most of the European countries had a more

homogeneous growth rate in cellular mobile phone subscription. In particular, the Nordic countries of Sweden, Norway, and Finland had high levels of cellular mobile phone subscribers. A few East Asian and Asia-Pacific countries (Japan, South Korea, Hong

Kong, and Singapore) exhibited high growth rates in mobile subscription, and the

Oceania countries (Australia and New Zealand) and the United States also had high rates of subscription. The Latin America region showed moderate mobile phone subscription

102

rates, but subscription rates were slow-moving in the Middle East and most of the

African countries.

There seems to be a significant relationship between cellular mobile phone

subscription rate and many of the variables investigated. According to the results of a

correlation matrix, technology infrastructure variables such as the number of Internet

users, personal computers, fixed telephone lines, television sets and DAI showed high

levels of correlation. That is, the more advanced the technological infrastructure, the

higher mobile diffusion rate in a country. In other words, we can estimate that the

concept of functional similarity worked on these technologies with regard to mobile

communication. However, the correlation results showed only the relationship between

each independent variable and mobile phone diffusion, excluding the effects of other

variables. Therefore, this study further examined the effect of other variables on the

relationship between each independent variable and cellular mobile diffusion.

The results of descriptive statistics and correlations have several important

implications. The pervasive mobile phone diffusion in the world implies that every country, including developed and developing countries, takes an interest in the mobile industry. In other words, mobile communication has been considered as a new communication channel during the 7-year period. In addition, mobile development and other technological infrastructure factors showed high correlations with each other. In

light of this fact, we can estimate that many countries, especially the developing

countries, might have invested in the mobile industry with the aim of a synergy effect.

Presently, the ICT industry converges with the mobile industry. To launch 3G mobile, a

country needs to support multimedia technology infrastructure. Similarly, the broadband

103 infrastructure is a prerequisite for the development of 4G mobile. Therefore, the development of mobile technology could be considered the catalyst of ICT development.

Meanwhile, in spite of overall mobile diffusion rates, the results also showed very diverse rates of mobile diffusion in different regions. This means that a regional digital divide of mobile diffusion exists in the world. Each country’s different business environment might cause the regional digital divide. Because each country has different economic potential, market competitiveness, and consumer demands, the degree of technological infrastructure development varies, and therefore, a difference in mobile diffusion is inevitable. Further, the high-mobile developed countries were located within the same region or close to each other, and this could be explained by the contagion effect

(Kauffman & Techatassanasoontorn, 2005). This effect describes the influence of one country on the diffusion growth of cellular mobile phone in another country. According to Kauffman & Techatassanasoontorn (2005), countries that have a high level of cross- border interaction with other countries are likely to experience a high regional contagion effect. The same notion is applicable for many East Asian and North American countries, as they showed similar patterns of mobile technology adoption and diffusion. In addition, geographical proximity is also related to cultural similarity. Cultural similarity predisposes the countries in a region to react to similar external factors in a patterned way

(Gruber & Verboven, 2001). In essence, the contagion effect and cultural similarity may help to explain the mobile gap between regions.

The fast and continuous mobile growth in the Nordic countries and some Asian countries owes much to these countries’ mobile policy. For example, the success of

Finland’s mobile industry in the 1990s originated from public policies such as Nordic

104

cooperation, EU strategies and Finnish liberalization in the 1970s and 1980s (Steinbock,

2003). In the case of Korea, the purpose of Korean telecommunication policy in 1990s

was ‘creating a competitive domestic market before allowing market access to foreign

competitor (Hyun and Lent, 1999).’ Based on the strong support of this policy driven by

the Korean government, the telecommunication infrastructure of Korea has been

strengthened and expanded. Finally, these regions have the most active mobile industries,

and have established the world’s mobile technology standard.

Hypotheses Testing and Research Questions

This study proposed 19 hypotheses and 3 research questions. For hypothesis

testing, two-way panel regression controlling for the 103 countries and the time factor (7-

year) was performed. The results showed that 6 hypotheses (H2, H5, H6, H8, H9, and

H10) were supported while 13 hypotheses (H1, H3, H4, H7, H11, H12, H13, H14, H15,

H16, H17, H18, and H19) were rejected. Specifically, FDI (H2), International

business/trade openness (EOPEN, H5), fixed telephone line penetration (FTL, H6),

Internet user penetration (IU, H8), television set penetration (TV, H9), and landline

telephone calls cost (LTCC, H10) were the factors affecting cellular mobile phone

diffusion from 1996 to 2002 at the country level.

The relationship between cellular mobile phone diffusion and each independent

variable was further tested by the OLS regression to assess the effect of the time factor.

According to the result, GDP per capita, Gini index (income inequality), political rights

(PR), civil rights (CL), personal computer penetration (PC), Internet user penetration

(IU), population over 65 years of age (POP3), television sets penetration (TV), landline

telephone calls cost (LTCC), and individualism (IDV) contributed to cellular mobile

phone diffusion.

105

Economic-Related Variables

With the results of these two regressions, this study can suggest several implications. In economic-related factors, GDP per capita (H1) and Gini (H3) were significant in OLS regression. GDP per capita indicates a country’s economic strength as well as individual wealth. Because mobile technology infrastructure is costly equipment, it stands to reason that the wealthier countries can have higher levels of mobile technology infrastructure. The Gini’s coefficient direction was different than anticipated.

Therefore, Gini did not support the cellular mobile phone diffusion. That means that income inequality and mobile diffusion were not related each other. On the contrary, FDI

(H2) and International business/trade openness factor (EOPEN, H5) were supported by the panel regression. This result showed that if the level of FDI and International business/trade openness are increased, the number of cellular mobile phone subscribers will also increase. In fact, these two factors are related to the flow of economic activities from one country to another. Therefore, we can assume that FDI and economic openness can serve as important channels of international mobile phone diffusion.

Overall, when the time factor was controlled, GDP per capita (H1) and Gini (H3) were not supported by this study, which is contrary to the result of most previous studies.

These factors might be significant in previous studies because the previous studies did not control for the time factor. Namely, GDP per capita and Gini might have been affected by time-change. When the time factor was controlled, FDI and EOPEN were effective. In light of this fact, the time factor plays an important role in economic-related factors that might affect mobile diffusion.

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Technology Infrastructure Variables

As in the correlation, the panel regression showed that most technology

infrastructure factors were significant, except for personal computers penetration (H7)

and DAI (H11). Fixed telephone line penetration (FTL, H6) positively influenced mobile

phone diffusion. In previous studies, the relationship between these two variables was reported in different ways. In this study, fixed line telephone and mobile phone were complementary to each other. However, in the OLS regression, FTL did not support mobile diffusion. Therefore, this positive relationship between FTL and mobile diffusion is only possible when controlling for the time factor.

As expected, Internet user penetration (IU, H8) had a positive relationship with

mobile phone diffusion in the panel regression. This is plausible as m-commerce has

already come to its mature stage in the 103 countries investigated. In fact, the Internet

plays an important role in mobile development because most multimedia and

m-commerce services using 3G and 4G mobile technology are based on Internet functions. Moreover, mobile has many advantages over stationary Internet in terms of connecting to the Internet whenever and wherever. The convergence of mobility and

Internet seems to be inevitable. Thus, Internet users could be more likely to move to mobile Internet. In the result of the OLS regression, Internet user penetration also supported mobile diffusion. Therefore, we can estimate that regardless of the time and country condition, IU levels became a barometer of development of the cellular mobile phone at the country level.

This study also showed that mobile phone diffusion was supported by television sets penetration (TV, H9) in the panel regression. This result points to the fact that

penetration of TV, the most content and entertainment-oriented medium was predictive of

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mobile diffusion. In fact, the world’s mobile industry already entered the 3G era – which

represents “Multimedia Mobile” – and now, multimedia mobile can provide various

contents to the mobile user. Therefore, we can estimate that mobile phone is attractive

enough and will get more subscribers from existing traditional media users (e.g., TV).

However, when time was not controlled, TV penetration contributed to mobile diffusion

but its coefficient was in the opposite direction, thus fail to support the mobile diffusion.

That means that more TV penetration leads to less mobile diffusion. From these two

different results, it seems that time makes TV become a more important factor in mobile

diffusion.

Contrary to our expectation, personal computer penetration (PC, H7) was not

significant in the panel regression. What is more, although PC was significant in the OLS

regression, it did not support mobile diffusion. These results are also quite opposite to

previous studies. Note that PC was correlated positively, but, when the effect of other

independent variables were considered simultaneously, the effect of PC on the mobile

phone diffusion was drastically reduced. First of all, we can assume that this may result

from the difference between mobile phone and PC in characteristics. One of the most

notable differences between mobile phone and PC is mobility. The PC is a typical

example of a stationary device. Of course, the laptop computer also has the mobility

characteristic, but there can be no comparison between the mobile phone and the laptop

computer in the numbers of their users. In spite of functional similarity, this directly opposite characteristic could make an insignificant or a negative relationship in the presented two regressions. In fact, mobile-centric development has been emphasized more than PC-centric development in the current ICT market because new markets need

108 new models of doing business and mobility is the most highlighted-feature in the ICT market.

Interestingly, this study found that landline telephone calls cost (LTCC, H10) positively affected mobile phone diffusion in both regressions. In other words, regardless of the time factor, the higher price of landline telephone calls likely lead to more effective mobile diffusion at the country level. Therefore, we can conclude that subscribers were likely to choose mobile phone when landline telephone prices were relatively expensive.

Regardless of the time factor, Digital Access Index (DAI, H11) did not support mobile diffusion. In fact, digital technology was not diffused all over the world.

According to the ITU (2001b), AMPS, one of the analog mobile technologies, was the dominant technology standard in North America (over 60%) and Latin America (over

55%) by the 1990’s. Thus, from 1996 to 2002, a high level of digital access was not a necessary condition to support cellular mobile phone diffusion.

Other Variables

This study showed the results of two regression analyses related to consumer- related variables, political environment variables, culture-related variables, and a market size variable. In consumer-related variables, age (H12) did not support mobile diffusion.

POP2 (H12, population 15-64) affected mobile diffusion negatively in the panel regression, and POP3 (H12, population ages over 65) showed the opposite direction of its coefficient in the OLS regression. Other consumer-related variables such as urban population (UPOP, H13) and education (EDU, H14) also did not support mobile diffusion in either of the two regressions. From these results, we can conclude that mobile diffusion has no connection with democratic factors, which is contrary to the previous studies. This may be explained by the fact that mobile phones have been distributed

109 regardless of the consumer’s age, education level, and residential district because of the effective values of the mobile phone.

Meanwhile, political environment variables such as political constraint (POLC,

H15), political rights (PR, H16), and civil liberties (CL, H16) were insignificant in the panel regression. However, when the time factor was not controlled, both political rights

(PR) and civil liberties (CL) contributed to cellular mobile diffusion. This study assumed that a lower level of PR and CL indexes was related to a higher level of mobile phone diffusion. However, the coefficient of CL showed a positive direction. Therefore, CL did not support the cellular mobile phone diffusion in this study. Basically, civil liberties mean protections from the power of government. Hence, we can estimate that when time was not controlled, government policy played a significant role in mobile phone diffusion. In the case of PR, the magnitude of the coefficient of PR in the OLS regression was close to zero and negative. Therefore, it is clear that the more politically free countries tend to diffuse mobile phones more because freedom or democracy can give people more of a chance to make choices by their own free will.

Finally, this study confirmed the insignificant role of culture-related variables such as individualism (IDV, H17) and uncertainty avoidance (UAI, H18), and the market size variable (TPOP, H19) in the panel regression. On the contrary, IDV was significant and supported mobile diffusion in the OLS regression, while others were insignificant. It was proved that when the time factor was not controlled, mobile diffusion was more active in a less individualized country.

In summary, this study suggested the factors that might affect cellular mobile phone penetration at the country level. The results of the two regressions were different.

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The results showed that GDP per capita, political rights and individualism were only supported by the OLS regression, while FDI, fixed line telephone, TV sets penetration, and international business/trade openness were only supported by the panel regression as factors affecting cellular mobile diffusion. In particular, Internet user penetration and landline telephone calls cost were significant and were supported by both the panel regression and the OLS regression. Although other variables such as population aged

15-64 in the panel regression and Gini index, civil liberties, personal computer penetration, population aged over 65, and television sets penetration in the OLS regression were also significant, their coefficients were of the opposite direction, thus the data failed to support the proposed hypotheses.

Basically, most variables were significant in previous studies. However, these variables were mainly investigated by the OLS regression that was not controlled by a time factor. When we considered the factor of time, the power of some variables seemed to diminish. In other words, when controlling for country and time, only economic factors and technology infrastructure factors supported mobile diffusion. We can consider the

‘leapfrogging’ concept as one of the most plausible reasons for these results. That is, we can assume that most developing countries have tried to catch up to a certain degree of

ICT development in a short period, and therefore, these countries have only concentrated their attention on economic and technological development without regard for other variables such as political and educational development. As we have seen in previous studies, because the economic and technological development has been thought to be a prerequisite for ICT development in the country, it is natural for these developing countries to act that way. The non-significant relationship between variables except

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economical and technological variables and mobile diffusion may be explained in the

same way by the fact that many countries became interested in mobile technology

development regardless of their urban population, educational characteristics, political

environment, cultural characteristics, and market size characteristics.

Among independent variables, only Internet user penetration (IU) and landline

telephone call cost (LTCC) were significant and supported mobile phone diffusion in the panel regression and the OLS regression. That is, these two variables proved to be the most effective variables irrespective of the time factor. In about a decade, the Internet has evolved from just a novel device to a major influence on nearly every aspect of life in the

examined countries. The Internet has become a social force, influencing how, when, and

why people communicate. Finally, it has become an economic force, changing the way

corporations operate and the way they interact with their customers. In this aspect, as

with the investigation of new media development including mobile telephone, it stands to

reason that the Internet must be considered as a top priority under any circumstances. In

the case of LTCC, we can think about two aspects: the functional similarity and the price.

When we compared the functionally similar media such as mobile phones and fixed

telephones, the price of the old medium (fixed telephone) may affect diffusion of the

relatively new medium (mobile), regardless of time factor. In other words, if the new

medium has a relatively reasonable price as well as good quality, the new medium will

more easily substitute the old medium.

Meanwhile, the time factor played a significant role between independent variables

and mobile diffusion in this study. However, the variance component for cross sections

was higher than the variance component for time-series. This implies that the time effect

112 to mobile diffusion was relatively low. Therefore, when we deal with cellular mobile phone diffusion, we can concentrate more on the cross section factor than on the time- series factor.

Major Factors Affecting Mobile Diffusion by Year

This study examined whether the relationships between mobile diffusion and each independent variable are affected by time. As the result of examination, we confirmed that there were differences in the results of the two regressions. Therefore, it seems that the relationships between mobile and each independent variable were affected by the time factor.

Further, this study showed the main factors affecting mobile diffusion by each year.

This is because, although the trend of mobile diffusion and its factors from 1996 to 2002 were examined, the factors that might affect mobile diffusion could be different in each year. When this study performed the OLS regression separately by year, only GDP per capita (1997-2001), DAI (1996, 1998-2002), Internet user penetration (1996-1999), and individualism (1996) contributed to mobile diffusion. Overall, because the R2 values from 1996 to 2002 increased gradually, it seems that each variable’s influence increased during the 7-year period. Similar to previous studies, we confirmed the significant role of

GDP per capita and Internet user penetration rates in mobile diffusion.

The result further showed that DAI was the most powerful indicator and that the β coefficient for DAI increased gradually through years. In former regressions, DAI played a minimal role in mobile diffusion. First, we can assume that it might be affected by other highly correlated variables. DAI and all consumer-related variables such as age, education level, and urban population were highly correlated with each other. TV penetration (TV) and landline telephone call cost (LTCC) also had a strong association

113

with DAI. Therefore, because the coefficient of β was too high, we can assume that there

might be collinearity between DAI and other independent variables. In the case of the

former two regressions, it was possible that DAI was basically an effective variable, but

because of other variables’ influences, its power was diminished. Second, digital technology might be used more pervasively from 1996 to 2002 than previously. This is a plausible estimation because the first 2G digital technology started in 1992 and the most widely used 2G digital technology, GSM, started to spread actively over the world in the mid-1990s. Third, in this study, DAI was only effective in the OLS regression that was conducted by each of the 7 individual years. Therefore, it seems that the time factor affected DAI. In other words, DAI may only be effective in a limited period. Because

DAI includes various characteristics, if the cross-sectional variables and time-series increase, its influence will most likely be reduced.

In short, the number of variables was reduced compared with the results of the former two regressions, and DAI was the most important variable in mobile diffusion.

Digital Divide and Mobile Diffusion

This study also examined the mobile phone subscription gap between developed

and developing countries. Although the average number of subscribers increased in both

developed and developing countries, the result indicates the existence of a digital divide,

with varying degrees, in cellular mobile phone subscriptions. Especially, there existed a

certain level of the digital divide within developing countries and there appeared to be a very wide gap between the South-East Asian and African countries, which are on the tail end of mobile subscription, and the European countries. Overall, although the mobile growth rates in the developing countries exceeded those in the developed countries during recent years, the gap of mobile phone penetration between the developed and

114

developing countries examined has actually widened significantly. Previous literature has

suggested several factors that may have caused the digital divide such as income,

infrastructure, expensive ICT access, and so on. In addition, because the top-rankers in

mobile diffusion created mobile technology and have dominated the world’s mobile

technology standard so far, other new entrants such as Latin America, Middle East, or

African countries have had trouble in overtaking the technological gaps. For instance,

although the Nordic countries, the United States, Japan and South Korea are ready to

launch a 4G technology era, most of the low subscription level countries remain at the 1G

or early-stage 2G technology era.

Moreover, this study showed “leapfrogging” in some developing countries. For

example, although the Czech Republic had a low level of fixed line penetration and a low

level of Internet user penetration, the number of mobile subscribers per 100 inhabitants

increased tremendously, from 1.9 in 1996 to 84.90 in 2002. Hungary was similar.

Contrary to the growth rates of mobile phone subscribers, the speed of other

infrastructure development was relatively slow.

Despite exhibiting “leapfrogging,” the degree of the digital divide with respect to mobile phone diffusion was not reduced from 1996 to 2002. In other words, although the leapfrogging effect might be working on the developing countries, there is still a wide gap between developed and developing countries because of low-level infrastructures and technical backwardness in the developing countries.

Contributions

This study makes contributions to ongoing research in the diffusion of innovation, especially as related to the ICT field. First, we identify two aspects of mobile diffusion processes in the 103 countries. The first aspect is the time, and the other aspect is

115 cross-sections. These two aspects helped us to understand mobile diffusion more specifically and furthermore, they could be used to verify the other ICT diffusion patterns. Based on these two aspects, this study used a broader kind of factor such as economic factors, technology infrastructure factors, and culture-related factors of cellular mobile diffusion than other previous ICT diffusion studies. Specifically, this study tested

22 specific factors in 8 categories. To verify and divide the effect of time and cross- section, this study performed the panel regression and the OLS regression, and compared these two regressions. Finally, the data set in this study included data from 103 countries from 1996 to 2002, and also included data from both developed and developing countries. This can give a strong basis for generalizing the results in the diffusion of cellular mobile phones, as well as the diffusion of other ICTs, in other countries which were not included.

Limitation and Suggestions for Future Research

There are several limitations in this study. First of all, because of the lack of data, this study did not include more countries such as those in the Middle East and Africa.

These countries have low levels of mobile penetration, but have impressive rates of increase in mobile subscribers. Moreover, this trend can be found in many other countries and is likely to continue. Therefore, for more advanced results, future studies must try to get the data from these regions. Similarly, although this study used the panel data set, the author included only the 7-year country data for this study. This might have caused minimal effect of time-series in panel regression. Therefore, future studies need to consider more years for more precise analysis.

This study could not use the proper demographic factors. For example, the population data could not represent its ages well. POP2 consisted of ages from 15 to 64.

116

This inadequate data resulted in the vague effects of ages to mobile phone diffusion in this study. In future studies, we need more specifically divided demographic data to verify the effect of ages, sex, and other demographic data.

In addition, this study used the ‘time-series cross-sectional regression’ for analysis, but the author used only basic functions. To capture the changes in mobile diffusion by country and time, further studies need to use more advanced panel regression. In this study, we can just know whether the time factor affected the mobile diffusion. We cannot know about the degree of change in mobile phone diffusion by country and time.

Lastly, this study did not include regulatory and policy factors. Previous studies indicated that each country’s regulatory and policy factors played a significant role in

ICT diffusion. However, because of lack of data and measurement problems, this study missed an opportunity to examine regulatory and policy factors. Future studies must investigate each country’s regulatory and policy factors and examine their influence on the other factors.

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BIOGRAPHICAL SKETCH

Yang-Hwan Lee received his B.A degree in mass communication from Kyung Hee

University in Seoul Korea. After obtaining his first Master of Arts degree in Mass

Communication from Kyung Hee University, he worked as a research assistant in

Planning and Development External Affairs at Kyung Hee University in 2002-2003. He received his second Master of Arts in Mass Communication degree in 2005, at the

University of Florida, specializing in telecommunication. After graduation, he plans to pursue a Ph.D program in mass communication at the University of South Carolina.

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