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An analysis of mobile Internet access in Thailand: Implications for bridging the digital divide
Telematics and Informatics 29 (2012) 254–262
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An analysis of mobile Internet access in Thailand: Implications for bridging the digital divide Chalita Srinuan ⇑, Pratompong Srinuan, Erik Bohlin Department of Technology Management and Economics, Chalmers University of Technology, SE-41296 Göteborg, Sweden
a r t i c l e
i n f o
Article history: Received 18 March 2011 Accepted 6 October 2011 Available online 20 October 2011 Keywords: Mobile internet Price elasticity Digital divide Thailand
a b s t r a c t Mobile Internet is growing around the world, bypassing the poor legacy of wired infrastructure. This growth can be observed even in developing countries like Thailand. To cope with this trend, this study attempts to provide guidance to a national regulatory agency (NRA) by addressing the following question: What are the key determinant factors for individual consumer to access mobile Internet? A discrete choice model is employed to examine empirically whether price, service, and application attributes, socio-economic variables, and service provider have a systematic link with the decision of the consumer. The data from a national survey in 2010 commissioned by the National Telecommunications Commission (NTC) of Thailand are used for the analysis. The results show that price, availability of fixed telephony, age, and living area are recognized as the strongest determinants for mobile Internet adoption. The findings also suggest that mobile Internet could be an alternative technology to bridge the digital divide, as the group of people that does not have fixed Internet connection at home can connect via mobile Internet. The price of mobile Internet service is inelastic, however, which means that an increase in price does not affect the propensity to access mobile Internet. This is a result of the lack of competition in fixed connection due to the concession, and it leads to limited choice for the consumer. Telecom regulators and policymakers therefore need to consider policies such as increasing competition and infrastructure investment in order to stimulate growth of mobile Internet adoption and close the digital divide in Thailand. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction With the growing penetration of wireless devices and the rapid technological development, wireless technology has shifted the world of fixed Internet access to mobile Internet access. Mobile Internet, which is generally defined as the use of the Internet via hand-held devices such as mobile phones, smart phones, personal digital assistants (PDAs), and laptops, is considered to be significantly different from fixed Internet, at least in terms of mobility and convenience. Mobile Internet provides not only voice communication but also data and video communications through mobile devices, for example, money transfer, location-based services, mobile search, mobile browsing, mobile health monitoring, and mobile payment. This has led to an astonishing growth rate for mobile Internet worldwide. It is clear that mobile Internet has driven fundamental changes in the mobile industry, business, individual lifestyles, and society at large. According to Gartner (2010), by 2013, mobile phones will overtake personal computers (PCs) as the most common web access device worldwide. The combined installed base of Smartphone and browser-equipped enhanced
⇑ Corresponding author. E-mail address: [email protected] (C. Srinuan). 0736-5853/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.tele.2011.10.003
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phones will exceed 1.82 billion units and be greater than the installed base for PCs, which is expected to be 1.78 billion units. This presents increasing consumer interest in mobile Internet and a new source of revenue for the industry. Moreover, the benefits of mobile Internet can be seen as offering an alternative for bridging the digital divide,1 in particular, for a developing country. Gunasekaran and Harmantzis (2007) note that three main issues must be considered to bridge the digital divide: accessibility, availability, and affordability of service and application. Thailand, as a developing country, is also confronted with the digital divide, both at a national and international level (Tangkitvanitch, 2005; Srinuan et al., 2010). At the end of 2009, the mobile penetration rate was 98.58%, while the fixed telephony and Internet penetration rates were 11.12% and 25.80%, respectively (NTC, 2010). This shows that the underdevelopment of fixed infrastructure has led to a low Internet penetration rate. On the other hand, there was an apparent leap-frogging of mobile over fixed both in terms of network coverage and subscription in Thailand. Furthermore, the current situation in Thailand reveals that mobile Internet subscribers make up 30% of the total number of mobile subscribers (NTC, 2010). The proportion of non-voice services has gradually increased, and it passed 20% at the end of 2010. This is faster than forecasted by the regulator (NTC, 2009, 2010). The two trends that have driven the growth of mobile Internet in Thailand are the introduction of third-generation mobile technology (3G) and improved coverage of mobile infrastructure, which also supports this argument. Hence, mobile Internet has the potential to close the digital gap. With regard to the introduction of 3G, despite the delay in issuing new 3G licenses in Thailand, the existing mobile operators are expected to migrate from 2.5G-based mobile systems to 3G-based systems or beyond, which will allow for multimedia transmission. As this convergence offers great potential for increasing Internet penetration rates and includes nonInternet users in the near future, it is argued that a valuable insight into current usage of mobile Internet can be gained to inform and guide the NRA and policymaker with regard to the digital divide. Against this background, the aim of this study is to examine the determinant factors for mobile Internet access in Thailand. The data used in this study are based on a survey sample of individual users commissioned by the NTC, the Thai telecom regulator, in 2010. An overview of mobile Internet in Thailand and a brief discussion of relevant academic studies are provided in the next section. Section 3 introduces the data and econometric method. Section 4 presents the findings and discusses the policy implications of the results. Finally, the study ends with a conclusion in Section 6.
2. Related literature 2.1. An overview of mobile Internet service in Thailand Mobile communication services were introduced in Thailand more than two decades ago. Mobile subscribers have increased gradually year on year, and, by 2010, the mobile penetration rate had reached 100%. At the same time, the number of mobile Internet users is growing. According to the NTC (2010), 30% of mobile subscribers use mobile Internet. This suggests that mobile Internet has the potential to become a common means of access to the Internet in Thailand, where the fixed Internet infrastructure is far from well developed (see Table 1). Mobile Internet access was first introduced to the Thai market in December 2000 by AIS, the largest nationwide mobile operator. AIS launched WAP (Wireless Application Protocol) service, so-called mobileLIFE, to its customers. In the initial period, mobileLIFE offered limited contents on this portal, e.g., headline news and video on demand, traffic reports, and messaging (AIS, 2003). AIS has continuously developed this application. In 2007, AIS introduced the latest version of mobileLIFE, which provided easy access to various mobile websites including mobile chat, news, music, games, movies, and Google mobile (AIS, 2007). AIS claimed that mobileLIFE was helping it to attract users to mobile Internet. DTAC, the second largest mobile operator, launched a mobile Internet portal, Djuice,2 in September 2001, also using WAP technology. The Djuice contents are similar to those of AIS’s mobileLIFE. The contents include live stock quotes, news, sports, entertainment, and third-party e-mail services (DTAC, 2001). This mobile Internet portal was live for a number of years, but it has now gone from the market and DTAC’s focus. The discontinuation of this service resulted from a lack of revenue and insufficient growth in the use of it in the market. Djuice was not developed by a Thai provider and its contents may not have suited Thai consumer preferences. Orange World was introduced as a multimedia service portal by TA Orange in 2004 (True corporation, 2004). TA Orange began to launch content in Orange World, including Photo World, Financial World, Game World, and Toon World. Customers of TA Orange could access this mobile Internet portal via GPRS (General Packet Radio Service) technology, a newer and higher capacity Internet connection. This mobile Internet portal has remained under the name trueworld, as the company was renamed TrueMove in 2006. In terms of mobile network development, mobile operators in Thailand have not only developed and adapted the mobile Internet portal but also improved the speed of mobile Internet connection in the last decade. DTAC was the first mobile operator to implement GPRS technology on its own nationwide network in 2001. It was also the first operator to improve its network by introducing EDGE (Enhanced Data rates for Global Evolution) technology in 2004. Two other major mobile Internet 1 The digital divide is well-documented in the trend to connect to the Internet in previous studies (Hoffman and Novak, 2000; Fox, 2005; Chinn and Fairlie, 2007). 2 Djuice is a mobile Internet portal developed by Telenor and adapted by DTAC for the Thai market.
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Table 1 Mobile subscribers, penetration, mobile data, and mobile per fixed subscriber.
Subscribers (millions) Penetration rate (%) Mobile Internet per mobile subscriber Household fixed line penetration
2003
2004
2005
2006
2007
2008
2009
2010
21.62 33.79 N/A 36.06
26.97 41.79 N/A 34.42
30.46 46.79 23.58 36.10
40.13 61.19 21.30 38.07
52.97 80.21 19.28 39.46
61.84 93.01 24.70 39.17
65.95 98.58 28.93 38.65
69.68 104.16 30.30 37.83
Source: NTC (2010).
providers, AIS and TrueMove, implemented GPRS in 2001 and 2004, respectively. AIS and TrueMove both launched EDGE in 2007. The gradual improvement in mobile networks has helped data transmission rates on mobile Internet to exceed those of the traditional dial-up connection. AIS’s and DTAC’s mobile Internet customers can access the Internet at an average speed of 100 and 150 kbps, respectively. This speed of connection is about 2–3 times faster than the dial-up speed. Mobile operators have run limited trials migrating existing networks to 3G networks in the 850 MHz (for TrueMove and DTAC) and 900 MHz bands (for AIS), and run major campaigns for bundle packages that include a low price Smartphone and Internet with 3G service. AIS and TrueMove have designed their bundle packages, which combine Wi-Fi and other telecom services, to provide convenient access to the Internet with a single bill payment. This indicates that all operators are preparing to move towards 3G technology (see Table 2). On the regulator side, the NTC, the Thai telecom regulator, has been preparing to award new 3G licenses for the past couple of years, but the political situation together with legal difficulties has led it to fail. In terms of legal difficulties, the Supreme Administrative Court has decreed that the NTC does not have authority to issue the 3G license. The National Broadcasting and Telecommunications Commission (NBTC) or a new regulatory body needs to be formed to oversee the matter, due to the 2007 Constitution (Bangkok Post, 2010). This delay could hamper network deployment by mobile operators and impede the growth of mobile Internet adoption. 2.2. Prior studies In order to examine the determinant factors of mobile Internet usage, a brief review of the relevant studies is discussed in this section. Earlier studies on mobile Internet can be divided into two groups based on their method and implication: studies on the technology acceptance model and on using the diffusion or econometric model. 2.2.1. Studies on the technology acceptance model (TAM) Studies on the TAM have an implication for business. Of these, the study by Cheong and Park (2005) is one of the earliest on mobile Internet using the TAM. They found that the attitude toward mobile Internet is the most significant factor for predicting intention behavior. Perceived playfulness also plays a positive role in developing attitude as well as intention, while the perceived price level has a negative role. Later studies by Shin (2007), and Phuangthong and Malisuwan (2008) confirm the findings of previous studies, in particular, the study by Phuangthong and Malisuwan, which provides empirical results for the case of Thailand. The studies by Funk (2005) and Okazaki (2006) also give details on individual behavior in Japan. Funk (2005) reveals that the push mail service and micro-payment system are the key drivers of mobile Internet growth. In terms of user adoption, affluent youth is the core segment of mobile Internet adoption (Okazaki, 2006).
Table 2 Comparison of major mobile Internet in Thailand. Characteristics
AIS
DTAC
TrueMove
Mobile subscribers Market share in 2010 (by subscriber) Year of entry Concession end Number of base stations Populated coverage (%) Mobile Internet application and year of launch Mobile Internet technology
30,425,700 43.66 1990 2016 15,400 97 mobileLIFE (2000)
20,935,813 30.04 1991 2018 10,082 N/A Djuice(2001)
16,537,382 23.73 2002 2018 N/A N/A trueworld (2004)
WAP(2000)/GPRS(2002)/ EDGE(2007) 100 kbps Yes (2007) with Wi-Fi
WAP(2001)/GPRS(2001)/ EDGE(2004) 150–160 kps N/A
GPRS(2004)/EDGE(2007)
2008
2008
Speed (average) Bundle package with other telecom services Introduced Internet SIM
Note: N/A refers to not available. Source: company websites and compiled by the authors.
N/A Yes (2004) with Wi-F/TV/broadband/fixed telephone 2008
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2.2.2. Studies using the diffusion or econometric model Studies using the diffusion or econometric model analyze the behavior of mobile Internet users and make suggestions to policymakers. Apart from an understanding of user acceptance of mobile Internet, very few studies provide an insight into behavior comparisons between mobile Internet users and non-users. Most discuss the gap between Internet users and nousers via fixed Internet (including fixed broadband). For example, Madden and Simpson (1997) show that income, installation fee, and age are factors that determine Internet usage. Rice and Katz (2003), Rappoport et al. (2003), and Cerno and Pérez Amaral (2006) also confirm that income and age are significant factors explaining the gap between Internet users and nonusers. The latter two also find that level of education can explain the behavior of Internet users. Currently, some mobile handsets allow connections via 3G technologies as well as Wi-Fi and Bluetooth radio interfaces. A number of studies have been conducted to investigate the adoption process of both mobile Internet users and non-users and contribute their results to the policymakers. Wareham et al. (2004) suggest that non-Internet users in American households have adopted 2G mobile communication devices at rates equal to or faster than the base population. As such, a migration path from 2G devices to mobile Internet may be the most practical way to Internet connectivity for non-users. Ishii (2004) also gives an example of the Japanese market, and his results demonstrate that mobile Internet is a more timeenhancing activity than PC Internet, which is a more time-displacing activity. Service application is another factor that influences the adoption of mobile Internet. The results by Hsu et al. (2007) indicate that users in Taiwan are usually concerned about the usefulness of the Multimedia Message Service (MMS), which is another key element in motivating the majority of adopters and potential adopters. Cardona et al. (2009) recently presented a certain acceptance of mobile broadband as a further access alternative in the Austrian market. Specifically, there appears to be some potential for substitution with regard to private users, while business users are more likely to continue their complementary use of both fixed and mobile broadband. These previous studies show that mobile Internet is becoming an alternative way for consumers to access the Internet both in developed and developing countries. The significant factors for mobile Internet adoption are the consumers’ attitudes toward service applications and their socio-economic backgrounds. This study also considers the importance of fixed telephony as a basic connection to the Internet. Case studies from many countries show that one of the obstacles to Internet readiness between countries or different parts of a country is a lack of or underdeveloped fixed infrastructure (Frieden, 2005; Lai and Brewer, 2006). The fixed line facilities may reveal a country’s technological development. In terms of voice communication, it can reflect the consumer’s decision to use fixed or mobile telephony (Ahn and Lee, 1999). It can also determine mobile Internet adoption in a country with an underdeveloped fixed infrastructure. Moreover, there is less research focus on mobile Internet adoption in developing countries, which confront the lack of fixed infrastructure and, at the same time, have high mobile penetration rates and discuss the possible role of mobile Internet in closing the digital divide. This study aims to fill this gap by taking nationwide survey data of individuals, focusing on the behavior of mobile Internet users, in order to identify the pulling forces driving access to and adoption of mobile Internet.
3. Data and method 3.1. Data A nationwide face-to-face interview-based survey of people in Thailand was commissioned by the NTC in 2010, and this survey was administered by the Thammasat University during May and June the same year. The questionnaire consisted of ten parts. Nine asked about telecom and media services such as fixed telephony, mobile telephony, the Internet, public phone, radio, and television, and the rest were about socio-economic backgrounds. The respondents were selected across each region of Thailand: Bangkok, Central, North, Northeast, and South. The sample consisted of two groups of respondents: consumers who accessed mobile Internet as their primary Internet connection, connecting either via a mobile device or USB cards, and consumers with a fixed Internet connection as their primary access, due to the aim of this study. All the respondents were under 15 years, and non-Internet users and incomplete answers were excluded from the sample. The total sample size after data cleaning was 739 respondents. Table 3 compares selected respondent characteristics for the survey with the National Statistic Office (NSO) database in 2009. The survey drew data from 9 provinces, including Bangkok, and the typical respondent was 30 years old with a bachelor’s degree and a monthly income of 12,384.90 THB. Overall, the sample characteristics were reasonably representative of the Thai population. Of this sample, 17.19% of the respondents had access to mobile Internet and 82.81% of the respondents did not have access to mobile Internet.3 The demographics of the mobile Internet users are shown in Table 4. In terms of gender, more males (18.34%) are mobile Internet users than females (16.15%), but the difference is not obvious, as shown in Table 5. Mobile Internet seems to have a positive association with the respondent’s age. For example, the percentage of mobile Internet users aged 15–19 is 10.26% compared with 20.14% and 23.35% for respondents aged from 20 to 29 and from 30 to 39. The proportion of people with mobile Internet access falls as the age increases however. A cross-tabulation between individual income and mobile Internet access seems to indicate that mobile Internet is more prevalent among respondents with a high income. For example, about 12.02% of the respondents with an individual income 3
Non-mobile Internet access includes Dial-up (2.98%), ADSL (78.21%), and Hotspots (1.62%).
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C. Srinuan et al. / Telematics and Informatics 29 (2012) 254–262 Table 3 Selected sample and population characteristics. Demographic background
Sample
NSO
Female (%)
47.23 (49.96) 29.43 (11.83) 47.77 (49.98) 12384.9 (10437.82)
50.70 – 30.83 – 48.83 – 12,000
Average age (year) Undergraduate degree Individual monthly income (THB)
Note: Standard deviation in parenthesis. Source: NSO, 2009.
Table 4 Summary of demographics of mobile Internet users. Demographic factors
Mobile Internet access (%)
Gender Female Male
18.34 16.15
Age 15–19 20–29 30–39 40–49 50–59
10.26 20.14 23.35 15.63 2.94
60–69
10.00
Income <5000 5001–10,000 10,001–15,000 15,001–20,000 20,001–25,000 >25,000
12.50 18.43 21.67 17.65 18.84 14.77
Education Below undergraduate Undergraduate Above undergraduate
Demographic factors Region Bangkok Central North North-East South Service and application factors Has fixed telephony at home Has no fixed telephony at home E-mail Search Social network Provider AIS DTAC TrueMove Hutch
Mobile Internet access (%) 29.21 7.18 17.04 21.82 17.28
11.13 40.00 16.38 17.12 15.65
47.24 40.16 10.24 2.36
14.29 17.56 25.88
below 5000 THB have access to mobile Internet, which is less than for other income groups. Another factor is level of education. Level of education tends to affect mobile Internet access, as respondents with a higher education are more likely to have mobile Internet access. Moreover, 29% of mobile Internet access is for respondents who live in Bangkok. Table 4 shows the relationship between mobile Internet access and mobile operator, with 47.24% of the respondents being customers of AIS, followed by DTAC (40.16%), TrueMove (10.24%), and Hutch (2.36%). Table 4 also presents mobile Internet access by service and application factor and shows a positive association between the unavailability of fixed line facilities at home and mobile Internet access (40.00%). Of the respondents with a fixed line at home, 11.13% also access mobile Internet. The respondents were asked about nine applications they used frequently via mobile Internet, for example, email, search, social networking, online video, and radio. Only the three that the respondents used most, e-mail, search, and social networking, were included in this study. Of these three applications, the search application was most popular among mobile Internet users at about 17.12%, followed by e-mail, and connecting to social networks, at 16.38% and 15.65%, respectively. 3.2. Method To analyze the binary outcome variable (access or not access to mobile Internet), this study employs the binomial logit model, which is based on discrete choice theory. Discrete choice theory is the study of behavior in situations in which the individual must select from a finite set of choices. It assumes that an individual is likely to choose one alternative over others when the level of its utility is greater to him than the utility of other alternatives.
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C. Srinuan et al. / Telematics and Informatics 29 (2012) 254–262 Table 5 Description of variables. Variable
Description
Mean
Std.Dev.
Dummy for mobile Internet access (Dependent variable)
= 0 if the respondent does not access mobile Internet as the primary access = 1 if the respondent accesses mobile Internet as the primary access
0.1719
0.3775
Service and application factors PAYMENT FIXED_TELEPHONE EMAIL SEARCH SOCIAL
Monthly expense for mobile Internet = 1 if the respondent has fixed telephone at home; = 0 otherwise = 1 if the respondent uses e-mail every day; = 0 otherwise = 1 if the respondent uses a search engine website everyday; = 0 otherwise = 1 if the respondent uses a social network website every day; = 0 otherwise
458.4500 0.7903 0.8593 0.9405 0.7524
362.6000 0.4074 0.3480 0.2368 0.4320
Socio-economic factors MALE LINCOME MINCOME Hhless25 Hhmore50 ED CENTRAL NORTH NORTH-EAST SOUTH
=1 =1 =1 =1 =1 =1 =1 =1 =1 =1
0.5277 0.2815 0.6184 0.4100 0.0771 0.5927 0.2449 0.3018 0.2233 0.1096
0.4996 0.4500 0.4861 0.4922 0.2670 0.4917 0.4303 0.4593 0.4167 0.3126
Mobile Internet provider DTAC TRUEMOVE HUTCH
= 1 if the respondent is a subscriber of Dtac; = 0 otherwise = 1 if the respondent is a subscriber of TrueMove; = 0 otherwise = 1 if the respondent is a subscriber of Hutch; = 0 otherwise
0.3789 0.1434 0.0122
0.4854 0.3508 0.1098
if if if if if if if if if if
the the the the the the the the the the
respondent respondent respondent respondent respondent respondent respondent respondent respondent respondent
is male; = 0 otherwise has a monthly income below 5000 THB; = 0 otherwise has a monthly income of 5000–25,000 THB; = 0 otherwise is aged less than 25 years; = 0 otherwise is aged over 55 years; = 0 otherwise has a bachelor’s degree or higher; = 0 otherwise lives in the central region; = 0 otherwise lives in the north region; = 0 otherwise lives in the north-east region; = 0 otherwise lives in the south region; = 0 otherwise
The logistic regression analysis is a technique that allows for the estimation of the probability that an event does or does not occur by predicting a binary dependent outcome from a set of independent variables. In the example of mobile Internet, the dependent variable is defined as access or not access to mobile Internet in relation to technology, socio-economic factors, and mobile operators. The level of utility that the nth respondent obtains from access or not access to mobile Internet service can be expressed by the following indirect utility function in terms of Z jn (price, service, and application factors, and mobile operators, jfaccess; not accessg) and Sn (demographic characteristics).
U jn ¼ UðZ jn ; Sn Þj ¼ faccess; not accessg
ð1Þ
The indirect utility in (1) can be divided into an observed part (V jn ) and an unobserved part (ejn ).
U jn ¼ V jn þ ejn
ð2Þ
The probability of use by the nth respondent, as derived by making the level of utility from using it greater than that from not using it, can be expressed as follows:
ProbðaccessjjÞ ¼ ProbðU access > U not access;n Þ
ð3Þ
When the unobserved ejn is independently and identically distributed according to the cumulative logistic distribution, the functional relationship between the revealed utility and the likelihood of using it is the binomial logit. A binomial logit model is used to relate the probability of an individual respondent accessing a mobile Internet service to explanatory factors, including technology factors, mobile operator, and demographic variables. The model is of the form:
Pjn ¼ Fðx0jn bÞ
ð4Þ
where P jn is the probability that the nth respondent will access mobile Internet service j and xjn is a vector of price, service and application attributes, mobile operator, and socio-demographic characteristics. b is the parameter vector to be estimated and F(.) is the cumulative logistic distribution function. In (4), the parameters relate changes in the explanatory variables to the direction of change in the using probability. According to Duffy-Deno (2001), the individual level price elasticity for mobile Internet access is calculated by applying the following:
¼ bp Price ð1 pÞ
ð5Þ
where bp is the coefficient of price derived from the estimated logit model and p is the predicted probability. This model aims to reveal the important variables that impact decisions to access mobile Internet. The value of the dependent variable was set to 1 when the respondent had decided to access mobile Internet service and to 0 otherwise. The explanatory variables of price, service, and application attributes, mobile operators, and demographic factors included in the model are listed and described briefly in Table 5.
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4. Findings and discussion 4.1. Findings The regression result indicates that R2 is 0.3164. This means that approximately 31.64% of the decision on whether to access mobile Internet is explained by the model. The estimated results are reported in Table 6 by transforming them into the marginal effects that can be explained as the change in magnitude of the dependent variable if the explanatory variable changes. The results show that the demand for access to mobile Internet has an inverse relationship to the price of mobile Internet. The elasticity with respect to the price of mobile Internet, calculated from the coefficient for PAYMENT is 0.1898. This means that a 1% increase in the price of mobile Internet service will lead to a 0.1898% decrease in the demand for mobile Internet or inelasticity. An increase in price will not affect the propensity to access mobile Internet. A lower value for own price elasticity may result from a need for wireless infrastructure development and competition in this market. The findings also suggest that two of the service and application factors, which are availability of fixed telephony and email application, have an inversely statistic significance for mobile Internet access. For example, the probability of mobile Internet access tends to increase by 10.25% if fixed telephony (FIXED) is not available at home. The fixed line facility is an important factor for the consumer’s decision to connect to the Internet, as mentioned by Frieden (2005), and Lai and Brewer (2006). This study also extends the findings of Ahn and Lee (1999), and Lai and Brewer (2006) that under-development of fixed telephony infrastructure could stimulate the adoption of mobile Internet across the country. The respondent is also less likely to use an e-mail (EMAIL) application via mobile Internet. This result may suggest that the respondent prefers to access his e-mail account via a fixed Internet connection. The result is in contrast to the study by Funk (2005). This may be due to differences in social and culture factors between Thailand and Japan. The social network application also provides the same relation as e-mail, while the searching application has a positive sign, though these three applications are not statistically significant. Of the socio-economic factors, the effect of age is statistically significant for explaining the probability of mobile Internet usage. If the respondent is less than 25 years (ALESS25), the probability of using mobile Internet tends to decrease by 13.72%. This would suggest that respondents who are aged less than 25 are less likely to access mobile Internet compared with respondents who are aged from 25 to 49. In other words, the core segment for mobile Internet usage is people of working age (25–50 years old), which is different from the study of the Japanese market by Okazaki (2006). The sign of the coefficients for living region can also explain the behavior of the respondent. The respondents who live in the CENTRAL, NORTH-EAST, and SOUTH regions are less likely to access mobile Internet compared with the respondents who live in Bangkok. This suggests that although the mobile infrastructure has better coverage than the fixed infrastructure, the respondents in these three regions currently do not consider access to the Internet from their mobile phone as their main access.
Table 6 Estimated results. Explanatory variable
Marginal effect
STD error
t-Test
p-Value
PAYMENT⁄⁄⁄ FIXED-TELEPHONE⁄⁄⁄ EMAIL SEARCH SOCIAL MALE ALESS25⁄ AMORE50 LINCOME MINCOME ED CENTRAL⁄ NORTH NORTH-EAST⁄⁄ SOUTH⁄⁄ DTAC TRUEMOVE HUTCH Number of observations LR chi2 (18) Prob > chi2 Log likelihood Pseudo R2
0.0005 0.1025 0.0373 0.0406 0.0012 0.0036 0.1372 0.0520 0.0069 0.0158 0.0111 0.0808 0.0418 0.0800 0.1033 0.0160 0.0261 0.0710 = 739 = 214.53 = 0.0000 = 231.798 = 0.3164
0.00005 0.0250 0.0357 0.0498 0.0302 0.0235 0.0359 0.0595 0.0561 0.0412 0.0309 0.0454 0.0373 0.0369 0.0486 0.0253 0.0392 0.1026
10.04 4.10 1.04 0.82 0.04 0.15 3.82 0.87 0.12 0.38 0.36 1.78 1.12 2.17 2.13 0.63 0.67 0.69
0.000 0.000 0.296 0.415 0.969 0.878 0.000 0.382 0.903 0.703 0.719 0.075 0.262 0.030 0.034 0.526 0.505 0.489
Note: ⁄,
⁄⁄ ⁄⁄⁄
,
represent significance at 10%, 5%, and 1% levels, respectively.
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Moreover, the magnitudes of the coefficients show that if the respondent is a subscriber of DTAC or TrueMove, he is less likely to access mobile Internet compared with mobile subscribers of AIS. The results confirm that this does not significantly influence the respondent’s decision of whether he will access mobile Internet. To sum up, the findings from this study provide similar results to those of previous studies. The estimated results show that the important factors affecting mobile Internet access in Thailand are price, availability of fixed telephony, age, living area, and mobile operator. 4.2. Discussion: possibilities for bridging the digital divide The implications of the results can be twofold: implications for the regulator and implications for business. 4.2.1. Implications for the regulator According to the results, mobile Internet can be seen as an option for closing the digital divide, in particular, for areas in which fixed telephony is not available. The current mobile Internet situation confirms that the three main conditions proposed by Gunasekaran and Harmantzis (2007), which are accessibility, availability, and affordability of service and applications, are applicable to Thailand. There are therefore several regulations and policies that the regulator and policymakers could consider to bridge the digital divide in the country. In terms of accessibility and availability, the wireless infrastructure, in particular Wi-Fi hotspots, only provides full coverage in populated areas. People in other parts of the country where fixed line infrastructure is not available can therefore only access the Internet if they have a mobile Internet device, i.e., Smartphone, and wireless infrastructure is available. The current 3G service offerings are very limited, however, due to the legal difficulties of allocating the new 3G frequencies. This has unfortunately resulted in EDGE currently being the best option for nationwide coverage. In terms of affordability, the results show that the price of mobile Internet service is inelastic. In terms of speed and price, the speed of wireless Internet connection is 2–3 times higher than that of dial-up Internet connection, while the price of mobile Internet is about the same as that of dial-up. The average monthly payment is 458 THB or 3.82% of the individual monthly income, which is cheaper than an ADSL connection. From the results, mobile Internet can play an important role in bridging the digital divide in Thailand in the short term. There is a weak response to price change of mobile Internet service however. This suggests that the mobile Internet user has a limited choice. The demand for Internet has grown rapidly since 2001, but there is an issue of concession that creates some difficulties for the Internet service providers to offer fixed Internet connection (Srinuan, 2011). The regulator therefore needs to promote fiercer competition in the environment in order to create more options and drive the price down. In the long term, the quality of service of mobile Internet needs to be announced explicitly by the regulator and ensure that consumers gain the benefits. 4.2.2. Implications for business Even though the results show that content and applications, i.e., email and social network applications, may not be the determinant factors for mobile Internet, many applications are still being developed and provided on the Internet today. As mobile Internet grows in popularity, the content and applications become the focus of many players in the value chain, including mobile service providers. An application can be free because the developer is offering it at no cost to the consumer while charging for other things within the application, e.g., advertising. Free application and content development may encourage consumers to have more experience of mobile Internet. Nevertheless, language could be a barrier for Thai consumers to use mobile Internet. This suggests that there are still opportunities for the industry to catch this group of users by developing applications that serve consumer needs. These issues need to be investigated further however. 5. Conclusion The transition from fixed to wireless technology is now taking over not only in voice communication but also in data communication and on the Internet. This phenomenon may be a good sign for bridging the digital divide by means of wireless technology. However, it is difficult to develop policies to accelerate the diffusion of mobile Internet without understanding the underlying factors that explain mobile Internet adoption. This study has examined mobile Internet adoption in Thailand based on a nationwide survey of individuals in 2010. The result of the logit regression model shows that price of mobile Internet service, availability of fixed telephony, age of user, living area, and mobile operator are the important drivers for mobile Internet usage and adoption. Specifically, the results indicate that price of mobile service is inelastic and mobile Internet could be a potential means to bridge the digital divide in areas that lack fixed line facilities. The results also provide a better understanding of application attributes, as useful and easy-to-use applications could encourage people to connect to the Internet via mobile phone. In the case of Thailand, the application is still at an early stage and has not influenced mobile Internet adoption as expected. Moreover, these results suggest that the NRA needs to stimulate the growth of mobile Internet. In the short term, frequency allocation will be an urgent task for the NRA in order to serve consumer needs. In parallel, the NRA needs to consider how to encourage investment in fixed and wireless infrastructure to eliminate the limitation of bandwidth capacity in the long run. To make the analysis more dynamic, future research could make a distinction between traditional mobile users and
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mobile Internet users with a longer period of study. The results would then be useful for regulators and policymakers to analyze mobile Internet adoption. Acknowledgments The authors would like to thank the National Telecommunications Commission of Thailand (NTC) for providing valuable data support, as well as the survey team at the Thammasat University, and Professor Masatsugu Tsuji and Ms. Orada Teppayayon of for their helpful comments. The early version of this paper was presented in the 2011 ITS Asia-Pacific Regional Conferences, 26–28 June, 2011 Taiwan. References Ahn, H., Lee, M., 1999. An econometric analysis of the demand for access to mobile telephone networks. Information Economics and Policy 11 (3), 297–305. Advance Info Service (AIS), 2003. AIS Annual Report. Retrieved 20 February 2001 from. Advance Info Service (AIS), 2007. AIS Annual Report. Retrieved 20 February 2001 from . Bangkok Post (2010). Court outlines reasons for freezing 3G auction. Retrieved 26 February 2011 from . Frieden, R., 2005. Lessons from broadband development in Canada, Japan, Korea and the United States. Telecommunications Policy 29 (8), 595–613. Funk, J.L., 2005. The future of the mobile phone Internet: an analysis of technological trajectories and lead users in the Japanese market. Technology in Society 27 (1), 69–83. Gartner Inc. (2010). Gartner’s Top Predictions for IT Organizations and Users, 2010 and Beyond: A New Balance. Retrieved 21 February 2011 from . Gunasekaran, V., Harmantzis, F.C., 2007. Emerging wireless technologies for developing countries. Technology in Society 29 (1), 23–42. Hoffman, D.L., Novak, T.P., 2000. The growing in digital divide: implications for an open research agenda. In: Brynjolfsson, E., Kahin, B. (Eds.), Understanding the Digital Economy: Data, tools, and Research. MIT Press, Cambridge, MA, pp. 245–299. Hsu, C.L., Lu, H.P., Hsu, H.H., 2007. Adoption of the mobile Internet: an empirical study of multimedia message service (MMS). Omega 35 (6), 715–726. Ishii, K., 2004. Internet use via mobile phone in Japan. Telecommunications Policy 28 (1), 43–58. Lai, B., Brewer, G., 2006. New York city’s broadband problem and the role of municipal government in promoting a private-sector solution. Technology in Society 28 (1–2), 245–259. Madden, M., Simpson, M., 1997. Residential broadband subscription demand: An econometric analysis of Australian choice experimental data. Applied Economics 29 (8), 1073–1078. National Telecommunications Commission of Thailand (NTC), 2009. Thai Telecommunications Indicators Yearbook 2009. Bangkok, Thailand. National Telecommunications Commission of Thailand (NTC), 2010. Thai Telecommunications Indicators Yearbook 2010. Bangkok, Thailand. Okazaki, S., 2006. What do we know about mobile Internet adopters? A cluster analysis. Information & Management 43 (2), 127–141. Phuangthong, D., Malisuwan, S., 2008. User acceptance of multimedia mobile Internet in Thailand. International Journal of the Computer, the Internet and Management 16 (3), 22–33. Rappoport, P.N., Kridel, D.J., Taylor, L.D., Alleman, J., 2003. Residual demand for access to the Internet. In: Madden, G. (Ed.), Emerging Telecommunications Networks: The International Handbook of Telecommunications Economics, vol. II. Edward Elgar Publishers, Cheltenham, pp. 1–20. Rice, R.E., Katz, J.E., 2003. Comparing Internet and mobile phone usage: digital divides of usage, adoption, and dropouts. Telecommunications Policy 27 (8– 9), 597–623. Shin, D.H., 2007. User acceptance of mobile internet: implication for convergence technologies. Interacting with Computers 19 (4), 472–483. Srinuan, C., Rohman, I.K., Srinuan, P., Bohlin, E., 2010. Digital divide in ASEAN countries: explaining the gap. In: Gugler, P., Chaisse, J. (Eds.), Competitiveness of ASEAN Countries: Corporate and Regulatory Drivers. Edward Elgar, London, pp. 153–172. Srinuan, C., 2011. Private costs of delayed privatization of TOT Public Company Limited. Info 13 (1), 74–91. Total Access Communications Plc. (DTAC), 2001. DTAC Annual Report. Retrieved 20 February 2011 from . Tangkitvanitch, S., 2005. Analyzing the problem of digital divides in Thailand. TDRI Quarterly Review 20 (2), 12–14. True Corporation, 2004. True Annual Report. Retrieved 20 February 2001 from . Wareham, J., Armando, L., Shi, W., 2004. Wireless diffusion and mobile computing: implications for the digital divide. Telecommunications Policy 28 (5–6), 439–457.
Contents lists available at SciVerse ScienceDirect
Telematics and Informatics journal homepage: www.elsevier.com/locate/tele
An analysis of mobile Internet access in Thailand: Implications for bridging the digital divide Chalita Srinuan ⇑, Pratompong Srinuan, Erik Bohlin Department of Technology Management and Economics, Chalmers University of Technology, SE-41296 Göteborg, Sweden
a r t i c l e
i n f o
Article history: Received 18 March 2011 Accepted 6 October 2011 Available online 20 October 2011 Keywords: Mobile internet Price elasticity Digital divide Thailand
a b s t r a c t Mobile Internet is growing around the world, bypassing the poor legacy of wired infrastructure. This growth can be observed even in developing countries like Thailand. To cope with this trend, this study attempts to provide guidance to a national regulatory agency (NRA) by addressing the following question: What are the key determinant factors for individual consumer to access mobile Internet? A discrete choice model is employed to examine empirically whether price, service, and application attributes, socio-economic variables, and service provider have a systematic link with the decision of the consumer. The data from a national survey in 2010 commissioned by the National Telecommunications Commission (NTC) of Thailand are used for the analysis. The results show that price, availability of fixed telephony, age, and living area are recognized as the strongest determinants for mobile Internet adoption. The findings also suggest that mobile Internet could be an alternative technology to bridge the digital divide, as the group of people that does not have fixed Internet connection at home can connect via mobile Internet. The price of mobile Internet service is inelastic, however, which means that an increase in price does not affect the propensity to access mobile Internet. This is a result of the lack of competition in fixed connection due to the concession, and it leads to limited choice for the consumer. Telecom regulators and policymakers therefore need to consider policies such as increasing competition and infrastructure investment in order to stimulate growth of mobile Internet adoption and close the digital divide in Thailand. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction With the growing penetration of wireless devices and the rapid technological development, wireless technology has shifted the world of fixed Internet access to mobile Internet access. Mobile Internet, which is generally defined as the use of the Internet via hand-held devices such as mobile phones, smart phones, personal digital assistants (PDAs), and laptops, is considered to be significantly different from fixed Internet, at least in terms of mobility and convenience. Mobile Internet provides not only voice communication but also data and video communications through mobile devices, for example, money transfer, location-based services, mobile search, mobile browsing, mobile health monitoring, and mobile payment. This has led to an astonishing growth rate for mobile Internet worldwide. It is clear that mobile Internet has driven fundamental changes in the mobile industry, business, individual lifestyles, and society at large. According to Gartner (2010), by 2013, mobile phones will overtake personal computers (PCs) as the most common web access device worldwide. The combined installed base of Smartphone and browser-equipped enhanced
⇑ Corresponding author. E-mail address: [email protected] (C. Srinuan). 0736-5853/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.tele.2011.10.003
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phones will exceed 1.82 billion units and be greater than the installed base for PCs, which is expected to be 1.78 billion units. This presents increasing consumer interest in mobile Internet and a new source of revenue for the industry. Moreover, the benefits of mobile Internet can be seen as offering an alternative for bridging the digital divide,1 in particular, for a developing country. Gunasekaran and Harmantzis (2007) note that three main issues must be considered to bridge the digital divide: accessibility, availability, and affordability of service and application. Thailand, as a developing country, is also confronted with the digital divide, both at a national and international level (Tangkitvanitch, 2005; Srinuan et al., 2010). At the end of 2009, the mobile penetration rate was 98.58%, while the fixed telephony and Internet penetration rates were 11.12% and 25.80%, respectively (NTC, 2010). This shows that the underdevelopment of fixed infrastructure has led to a low Internet penetration rate. On the other hand, there was an apparent leap-frogging of mobile over fixed both in terms of network coverage and subscription in Thailand. Furthermore, the current situation in Thailand reveals that mobile Internet subscribers make up 30% of the total number of mobile subscribers (NTC, 2010). The proportion of non-voice services has gradually increased, and it passed 20% at the end of 2010. This is faster than forecasted by the regulator (NTC, 2009, 2010). The two trends that have driven the growth of mobile Internet in Thailand are the introduction of third-generation mobile technology (3G) and improved coverage of mobile infrastructure, which also supports this argument. Hence, mobile Internet has the potential to close the digital gap. With regard to the introduction of 3G, despite the delay in issuing new 3G licenses in Thailand, the existing mobile operators are expected to migrate from 2.5G-based mobile systems to 3G-based systems or beyond, which will allow for multimedia transmission. As this convergence offers great potential for increasing Internet penetration rates and includes nonInternet users in the near future, it is argued that a valuable insight into current usage of mobile Internet can be gained to inform and guide the NRA and policymaker with regard to the digital divide. Against this background, the aim of this study is to examine the determinant factors for mobile Internet access in Thailand. The data used in this study are based on a survey sample of individual users commissioned by the NTC, the Thai telecom regulator, in 2010. An overview of mobile Internet in Thailand and a brief discussion of relevant academic studies are provided in the next section. Section 3 introduces the data and econometric method. Section 4 presents the findings and discusses the policy implications of the results. Finally, the study ends with a conclusion in Section 6.
2. Related literature 2.1. An overview of mobile Internet service in Thailand Mobile communication services were introduced in Thailand more than two decades ago. Mobile subscribers have increased gradually year on year, and, by 2010, the mobile penetration rate had reached 100%. At the same time, the number of mobile Internet users is growing. According to the NTC (2010), 30% of mobile subscribers use mobile Internet. This suggests that mobile Internet has the potential to become a common means of access to the Internet in Thailand, where the fixed Internet infrastructure is far from well developed (see Table 1). Mobile Internet access was first introduced to the Thai market in December 2000 by AIS, the largest nationwide mobile operator. AIS launched WAP (Wireless Application Protocol) service, so-called mobileLIFE, to its customers. In the initial period, mobileLIFE offered limited contents on this portal, e.g., headline news and video on demand, traffic reports, and messaging (AIS, 2003). AIS has continuously developed this application. In 2007, AIS introduced the latest version of mobileLIFE, which provided easy access to various mobile websites including mobile chat, news, music, games, movies, and Google mobile (AIS, 2007). AIS claimed that mobileLIFE was helping it to attract users to mobile Internet. DTAC, the second largest mobile operator, launched a mobile Internet portal, Djuice,2 in September 2001, also using WAP technology. The Djuice contents are similar to those of AIS’s mobileLIFE. The contents include live stock quotes, news, sports, entertainment, and third-party e-mail services (DTAC, 2001). This mobile Internet portal was live for a number of years, but it has now gone from the market and DTAC’s focus. The discontinuation of this service resulted from a lack of revenue and insufficient growth in the use of it in the market. Djuice was not developed by a Thai provider and its contents may not have suited Thai consumer preferences. Orange World was introduced as a multimedia service portal by TA Orange in 2004 (True corporation, 2004). TA Orange began to launch content in Orange World, including Photo World, Financial World, Game World, and Toon World. Customers of TA Orange could access this mobile Internet portal via GPRS (General Packet Radio Service) technology, a newer and higher capacity Internet connection. This mobile Internet portal has remained under the name trueworld, as the company was renamed TrueMove in 2006. In terms of mobile network development, mobile operators in Thailand have not only developed and adapted the mobile Internet portal but also improved the speed of mobile Internet connection in the last decade. DTAC was the first mobile operator to implement GPRS technology on its own nationwide network in 2001. It was also the first operator to improve its network by introducing EDGE (Enhanced Data rates for Global Evolution) technology in 2004. Two other major mobile Internet 1 The digital divide is well-documented in the trend to connect to the Internet in previous studies (Hoffman and Novak, 2000; Fox, 2005; Chinn and Fairlie, 2007). 2 Djuice is a mobile Internet portal developed by Telenor and adapted by DTAC for the Thai market.
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Table 1 Mobile subscribers, penetration, mobile data, and mobile per fixed subscriber.
Subscribers (millions) Penetration rate (%) Mobile Internet per mobile subscriber Household fixed line penetration
2003
2004
2005
2006
2007
2008
2009
2010
21.62 33.79 N/A 36.06
26.97 41.79 N/A 34.42
30.46 46.79 23.58 36.10
40.13 61.19 21.30 38.07
52.97 80.21 19.28 39.46
61.84 93.01 24.70 39.17
65.95 98.58 28.93 38.65
69.68 104.16 30.30 37.83
Source: NTC (2010).
providers, AIS and TrueMove, implemented GPRS in 2001 and 2004, respectively. AIS and TrueMove both launched EDGE in 2007. The gradual improvement in mobile networks has helped data transmission rates on mobile Internet to exceed those of the traditional dial-up connection. AIS’s and DTAC’s mobile Internet customers can access the Internet at an average speed of 100 and 150 kbps, respectively. This speed of connection is about 2–3 times faster than the dial-up speed. Mobile operators have run limited trials migrating existing networks to 3G networks in the 850 MHz (for TrueMove and DTAC) and 900 MHz bands (for AIS), and run major campaigns for bundle packages that include a low price Smartphone and Internet with 3G service. AIS and TrueMove have designed their bundle packages, which combine Wi-Fi and other telecom services, to provide convenient access to the Internet with a single bill payment. This indicates that all operators are preparing to move towards 3G technology (see Table 2). On the regulator side, the NTC, the Thai telecom regulator, has been preparing to award new 3G licenses for the past couple of years, but the political situation together with legal difficulties has led it to fail. In terms of legal difficulties, the Supreme Administrative Court has decreed that the NTC does not have authority to issue the 3G license. The National Broadcasting and Telecommunications Commission (NBTC) or a new regulatory body needs to be formed to oversee the matter, due to the 2007 Constitution (Bangkok Post, 2010). This delay could hamper network deployment by mobile operators and impede the growth of mobile Internet adoption. 2.2. Prior studies In order to examine the determinant factors of mobile Internet usage, a brief review of the relevant studies is discussed in this section. Earlier studies on mobile Internet can be divided into two groups based on their method and implication: studies on the technology acceptance model and on using the diffusion or econometric model. 2.2.1. Studies on the technology acceptance model (TAM) Studies on the TAM have an implication for business. Of these, the study by Cheong and Park (2005) is one of the earliest on mobile Internet using the TAM. They found that the attitude toward mobile Internet is the most significant factor for predicting intention behavior. Perceived playfulness also plays a positive role in developing attitude as well as intention, while the perceived price level has a negative role. Later studies by Shin (2007), and Phuangthong and Malisuwan (2008) confirm the findings of previous studies, in particular, the study by Phuangthong and Malisuwan, which provides empirical results for the case of Thailand. The studies by Funk (2005) and Okazaki (2006) also give details on individual behavior in Japan. Funk (2005) reveals that the push mail service and micro-payment system are the key drivers of mobile Internet growth. In terms of user adoption, affluent youth is the core segment of mobile Internet adoption (Okazaki, 2006).
Table 2 Comparison of major mobile Internet in Thailand. Characteristics
AIS
DTAC
TrueMove
Mobile subscribers Market share in 2010 (by subscriber) Year of entry Concession end Number of base stations Populated coverage (%) Mobile Internet application and year of launch Mobile Internet technology
30,425,700 43.66 1990 2016 15,400 97 mobileLIFE (2000)
20,935,813 30.04 1991 2018 10,082 N/A Djuice(2001)
16,537,382 23.73 2002 2018 N/A N/A trueworld (2004)
WAP(2000)/GPRS(2002)/ EDGE(2007) 100 kbps Yes (2007) with Wi-Fi
WAP(2001)/GPRS(2001)/ EDGE(2004) 150–160 kps N/A
GPRS(2004)/EDGE(2007)
2008
2008
Speed (average) Bundle package with other telecom services Introduced Internet SIM
Note: N/A refers to not available. Source: company websites and compiled by the authors.
N/A Yes (2004) with Wi-F/TV/broadband/fixed telephone 2008
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2.2.2. Studies using the diffusion or econometric model Studies using the diffusion or econometric model analyze the behavior of mobile Internet users and make suggestions to policymakers. Apart from an understanding of user acceptance of mobile Internet, very few studies provide an insight into behavior comparisons between mobile Internet users and non-users. Most discuss the gap between Internet users and nousers via fixed Internet (including fixed broadband). For example, Madden and Simpson (1997) show that income, installation fee, and age are factors that determine Internet usage. Rice and Katz (2003), Rappoport et al. (2003), and Cerno and Pérez Amaral (2006) also confirm that income and age are significant factors explaining the gap between Internet users and nonusers. The latter two also find that level of education can explain the behavior of Internet users. Currently, some mobile handsets allow connections via 3G technologies as well as Wi-Fi and Bluetooth radio interfaces. A number of studies have been conducted to investigate the adoption process of both mobile Internet users and non-users and contribute their results to the policymakers. Wareham et al. (2004) suggest that non-Internet users in American households have adopted 2G mobile communication devices at rates equal to or faster than the base population. As such, a migration path from 2G devices to mobile Internet may be the most practical way to Internet connectivity for non-users. Ishii (2004) also gives an example of the Japanese market, and his results demonstrate that mobile Internet is a more timeenhancing activity than PC Internet, which is a more time-displacing activity. Service application is another factor that influences the adoption of mobile Internet. The results by Hsu et al. (2007) indicate that users in Taiwan are usually concerned about the usefulness of the Multimedia Message Service (MMS), which is another key element in motivating the majority of adopters and potential adopters. Cardona et al. (2009) recently presented a certain acceptance of mobile broadband as a further access alternative in the Austrian market. Specifically, there appears to be some potential for substitution with regard to private users, while business users are more likely to continue their complementary use of both fixed and mobile broadband. These previous studies show that mobile Internet is becoming an alternative way for consumers to access the Internet both in developed and developing countries. The significant factors for mobile Internet adoption are the consumers’ attitudes toward service applications and their socio-economic backgrounds. This study also considers the importance of fixed telephony as a basic connection to the Internet. Case studies from many countries show that one of the obstacles to Internet readiness between countries or different parts of a country is a lack of or underdeveloped fixed infrastructure (Frieden, 2005; Lai and Brewer, 2006). The fixed line facilities may reveal a country’s technological development. In terms of voice communication, it can reflect the consumer’s decision to use fixed or mobile telephony (Ahn and Lee, 1999). It can also determine mobile Internet adoption in a country with an underdeveloped fixed infrastructure. Moreover, there is less research focus on mobile Internet adoption in developing countries, which confront the lack of fixed infrastructure and, at the same time, have high mobile penetration rates and discuss the possible role of mobile Internet in closing the digital divide. This study aims to fill this gap by taking nationwide survey data of individuals, focusing on the behavior of mobile Internet users, in order to identify the pulling forces driving access to and adoption of mobile Internet.
3. Data and method 3.1. Data A nationwide face-to-face interview-based survey of people in Thailand was commissioned by the NTC in 2010, and this survey was administered by the Thammasat University during May and June the same year. The questionnaire consisted of ten parts. Nine asked about telecom and media services such as fixed telephony, mobile telephony, the Internet, public phone, radio, and television, and the rest were about socio-economic backgrounds. The respondents were selected across each region of Thailand: Bangkok, Central, North, Northeast, and South. The sample consisted of two groups of respondents: consumers who accessed mobile Internet as their primary Internet connection, connecting either via a mobile device or USB cards, and consumers with a fixed Internet connection as their primary access, due to the aim of this study. All the respondents were under 15 years, and non-Internet users and incomplete answers were excluded from the sample. The total sample size after data cleaning was 739 respondents. Table 3 compares selected respondent characteristics for the survey with the National Statistic Office (NSO) database in 2009. The survey drew data from 9 provinces, including Bangkok, and the typical respondent was 30 years old with a bachelor’s degree and a monthly income of 12,384.90 THB. Overall, the sample characteristics were reasonably representative of the Thai population. Of this sample, 17.19% of the respondents had access to mobile Internet and 82.81% of the respondents did not have access to mobile Internet.3 The demographics of the mobile Internet users are shown in Table 4. In terms of gender, more males (18.34%) are mobile Internet users than females (16.15%), but the difference is not obvious, as shown in Table 5. Mobile Internet seems to have a positive association with the respondent’s age. For example, the percentage of mobile Internet users aged 15–19 is 10.26% compared with 20.14% and 23.35% for respondents aged from 20 to 29 and from 30 to 39. The proportion of people with mobile Internet access falls as the age increases however. A cross-tabulation between individual income and mobile Internet access seems to indicate that mobile Internet is more prevalent among respondents with a high income. For example, about 12.02% of the respondents with an individual income 3
Non-mobile Internet access includes Dial-up (2.98%), ADSL (78.21%), and Hotspots (1.62%).
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C. Srinuan et al. / Telematics and Informatics 29 (2012) 254–262 Table 3 Selected sample and population characteristics. Demographic background
Sample
NSO
Female (%)
47.23 (49.96) 29.43 (11.83) 47.77 (49.98) 12384.9 (10437.82)
50.70 – 30.83 – 48.83 – 12,000
Average age (year) Undergraduate degree Individual monthly income (THB)
Note: Standard deviation in parenthesis. Source: NSO, 2009.
Table 4 Summary of demographics of mobile Internet users. Demographic factors
Mobile Internet access (%)
Gender Female Male
18.34 16.15
Age 15–19 20–29 30–39 40–49 50–59
10.26 20.14 23.35 15.63 2.94
60–69
10.00
Income <5000 5001–10,000 10,001–15,000 15,001–20,000 20,001–25,000 >25,000
12.50 18.43 21.67 17.65 18.84 14.77
Education Below undergraduate Undergraduate Above undergraduate
Demographic factors Region Bangkok Central North North-East South Service and application factors Has fixed telephony at home Has no fixed telephony at home E-mail Search Social network Provider AIS DTAC TrueMove Hutch
Mobile Internet access (%) 29.21 7.18 17.04 21.82 17.28
11.13 40.00 16.38 17.12 15.65
47.24 40.16 10.24 2.36
14.29 17.56 25.88
below 5000 THB have access to mobile Internet, which is less than for other income groups. Another factor is level of education. Level of education tends to affect mobile Internet access, as respondents with a higher education are more likely to have mobile Internet access. Moreover, 29% of mobile Internet access is for respondents who live in Bangkok. Table 4 shows the relationship between mobile Internet access and mobile operator, with 47.24% of the respondents being customers of AIS, followed by DTAC (40.16%), TrueMove (10.24%), and Hutch (2.36%). Table 4 also presents mobile Internet access by service and application factor and shows a positive association between the unavailability of fixed line facilities at home and mobile Internet access (40.00%). Of the respondents with a fixed line at home, 11.13% also access mobile Internet. The respondents were asked about nine applications they used frequently via mobile Internet, for example, email, search, social networking, online video, and radio. Only the three that the respondents used most, e-mail, search, and social networking, were included in this study. Of these three applications, the search application was most popular among mobile Internet users at about 17.12%, followed by e-mail, and connecting to social networks, at 16.38% and 15.65%, respectively. 3.2. Method To analyze the binary outcome variable (access or not access to mobile Internet), this study employs the binomial logit model, which is based on discrete choice theory. Discrete choice theory is the study of behavior in situations in which the individual must select from a finite set of choices. It assumes that an individual is likely to choose one alternative over others when the level of its utility is greater to him than the utility of other alternatives.
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C. Srinuan et al. / Telematics and Informatics 29 (2012) 254–262 Table 5 Description of variables. Variable
Description
Mean
Std.Dev.
Dummy for mobile Internet access (Dependent variable)
= 0 if the respondent does not access mobile Internet as the primary access = 1 if the respondent accesses mobile Internet as the primary access
0.1719
0.3775
Service and application factors PAYMENT FIXED_TELEPHONE EMAIL SEARCH SOCIAL
Monthly expense for mobile Internet = 1 if the respondent has fixed telephone at home; = 0 otherwise = 1 if the respondent uses e-mail every day; = 0 otherwise = 1 if the respondent uses a search engine website everyday; = 0 otherwise = 1 if the respondent uses a social network website every day; = 0 otherwise
458.4500 0.7903 0.8593 0.9405 0.7524
362.6000 0.4074 0.3480 0.2368 0.4320
Socio-economic factors MALE LINCOME MINCOME Hhless25 Hhmore50 ED CENTRAL NORTH NORTH-EAST SOUTH
=1 =1 =1 =1 =1 =1 =1 =1 =1 =1
0.5277 0.2815 0.6184 0.4100 0.0771 0.5927 0.2449 0.3018 0.2233 0.1096
0.4996 0.4500 0.4861 0.4922 0.2670 0.4917 0.4303 0.4593 0.4167 0.3126
Mobile Internet provider DTAC TRUEMOVE HUTCH
= 1 if the respondent is a subscriber of Dtac; = 0 otherwise = 1 if the respondent is a subscriber of TrueMove; = 0 otherwise = 1 if the respondent is a subscriber of Hutch; = 0 otherwise
0.3789 0.1434 0.0122
0.4854 0.3508 0.1098
if if if if if if if if if if
the the the the the the the the the the
respondent respondent respondent respondent respondent respondent respondent respondent respondent respondent
is male; = 0 otherwise has a monthly income below 5000 THB; = 0 otherwise has a monthly income of 5000–25,000 THB; = 0 otherwise is aged less than 25 years; = 0 otherwise is aged over 55 years; = 0 otherwise has a bachelor’s degree or higher; = 0 otherwise lives in the central region; = 0 otherwise lives in the north region; = 0 otherwise lives in the north-east region; = 0 otherwise lives in the south region; = 0 otherwise
The logistic regression analysis is a technique that allows for the estimation of the probability that an event does or does not occur by predicting a binary dependent outcome from a set of independent variables. In the example of mobile Internet, the dependent variable is defined as access or not access to mobile Internet in relation to technology, socio-economic factors, and mobile operators. The level of utility that the nth respondent obtains from access or not access to mobile Internet service can be expressed by the following indirect utility function in terms of Z jn (price, service, and application factors, and mobile operators, jfaccess; not accessg) and Sn (demographic characteristics).
U jn ¼ UðZ jn ; Sn Þj ¼ faccess; not accessg
ð1Þ
The indirect utility in (1) can be divided into an observed part (V jn ) and an unobserved part (ejn ).
U jn ¼ V jn þ ejn
ð2Þ
The probability of use by the nth respondent, as derived by making the level of utility from using it greater than that from not using it, can be expressed as follows:
ProbðaccessjjÞ ¼ ProbðU access > U not access;n Þ
ð3Þ
When the unobserved ejn is independently and identically distributed according to the cumulative logistic distribution, the functional relationship between the revealed utility and the likelihood of using it is the binomial logit. A binomial logit model is used to relate the probability of an individual respondent accessing a mobile Internet service to explanatory factors, including technology factors, mobile operator, and demographic variables. The model is of the form:
Pjn ¼ Fðx0jn bÞ
ð4Þ
where P jn is the probability that the nth respondent will access mobile Internet service j and xjn is a vector of price, service and application attributes, mobile operator, and socio-demographic characteristics. b is the parameter vector to be estimated and F(.) is the cumulative logistic distribution function. In (4), the parameters relate changes in the explanatory variables to the direction of change in the using probability. According to Duffy-Deno (2001), the individual level price elasticity for mobile Internet access is calculated by applying the following:
¼ bp Price ð1 pÞ
ð5Þ
where bp is the coefficient of price derived from the estimated logit model and p is the predicted probability. This model aims to reveal the important variables that impact decisions to access mobile Internet. The value of the dependent variable was set to 1 when the respondent had decided to access mobile Internet service and to 0 otherwise. The explanatory variables of price, service, and application attributes, mobile operators, and demographic factors included in the model are listed and described briefly in Table 5.
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4. Findings and discussion 4.1. Findings The regression result indicates that R2 is 0.3164. This means that approximately 31.64% of the decision on whether to access mobile Internet is explained by the model. The estimated results are reported in Table 6 by transforming them into the marginal effects that can be explained as the change in magnitude of the dependent variable if the explanatory variable changes. The results show that the demand for access to mobile Internet has an inverse relationship to the price of mobile Internet. The elasticity with respect to the price of mobile Internet, calculated from the coefficient for PAYMENT is 0.1898. This means that a 1% increase in the price of mobile Internet service will lead to a 0.1898% decrease in the demand for mobile Internet or inelasticity. An increase in price will not affect the propensity to access mobile Internet. A lower value for own price elasticity may result from a need for wireless infrastructure development and competition in this market. The findings also suggest that two of the service and application factors, which are availability of fixed telephony and email application, have an inversely statistic significance for mobile Internet access. For example, the probability of mobile Internet access tends to increase by 10.25% if fixed telephony (FIXED) is not available at home. The fixed line facility is an important factor for the consumer’s decision to connect to the Internet, as mentioned by Frieden (2005), and Lai and Brewer (2006). This study also extends the findings of Ahn and Lee (1999), and Lai and Brewer (2006) that under-development of fixed telephony infrastructure could stimulate the adoption of mobile Internet across the country. The respondent is also less likely to use an e-mail (EMAIL) application via mobile Internet. This result may suggest that the respondent prefers to access his e-mail account via a fixed Internet connection. The result is in contrast to the study by Funk (2005). This may be due to differences in social and culture factors between Thailand and Japan. The social network application also provides the same relation as e-mail, while the searching application has a positive sign, though these three applications are not statistically significant. Of the socio-economic factors, the effect of age is statistically significant for explaining the probability of mobile Internet usage. If the respondent is less than 25 years (ALESS25), the probability of using mobile Internet tends to decrease by 13.72%. This would suggest that respondents who are aged less than 25 are less likely to access mobile Internet compared with respondents who are aged from 25 to 49. In other words, the core segment for mobile Internet usage is people of working age (25–50 years old), which is different from the study of the Japanese market by Okazaki (2006). The sign of the coefficients for living region can also explain the behavior of the respondent. The respondents who live in the CENTRAL, NORTH-EAST, and SOUTH regions are less likely to access mobile Internet compared with the respondents who live in Bangkok. This suggests that although the mobile infrastructure has better coverage than the fixed infrastructure, the respondents in these three regions currently do not consider access to the Internet from their mobile phone as their main access.
Table 6 Estimated results. Explanatory variable
Marginal effect
STD error
t-Test
p-Value
PAYMENT⁄⁄⁄ FIXED-TELEPHONE⁄⁄⁄ EMAIL SEARCH SOCIAL MALE ALESS25⁄ AMORE50 LINCOME MINCOME ED CENTRAL⁄ NORTH NORTH-EAST⁄⁄ SOUTH⁄⁄ DTAC TRUEMOVE HUTCH Number of observations LR chi2 (18) Prob > chi2 Log likelihood Pseudo R2
0.0005 0.1025 0.0373 0.0406 0.0012 0.0036 0.1372 0.0520 0.0069 0.0158 0.0111 0.0808 0.0418 0.0800 0.1033 0.0160 0.0261 0.0710 = 739 = 214.53 = 0.0000 = 231.798 = 0.3164
0.00005 0.0250 0.0357 0.0498 0.0302 0.0235 0.0359 0.0595 0.0561 0.0412 0.0309 0.0454 0.0373 0.0369 0.0486 0.0253 0.0392 0.1026
10.04 4.10 1.04 0.82 0.04 0.15 3.82 0.87 0.12 0.38 0.36 1.78 1.12 2.17 2.13 0.63 0.67 0.69
0.000 0.000 0.296 0.415 0.969 0.878 0.000 0.382 0.903 0.703 0.719 0.075 0.262 0.030 0.034 0.526 0.505 0.489
Note: ⁄,
⁄⁄ ⁄⁄⁄
,
represent significance at 10%, 5%, and 1% levels, respectively.
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Moreover, the magnitudes of the coefficients show that if the respondent is a subscriber of DTAC or TrueMove, he is less likely to access mobile Internet compared with mobile subscribers of AIS. The results confirm that this does not significantly influence the respondent’s decision of whether he will access mobile Internet. To sum up, the findings from this study provide similar results to those of previous studies. The estimated results show that the important factors affecting mobile Internet access in Thailand are price, availability of fixed telephony, age, living area, and mobile operator. 4.2. Discussion: possibilities for bridging the digital divide The implications of the results can be twofold: implications for the regulator and implications for business. 4.2.1. Implications for the regulator According to the results, mobile Internet can be seen as an option for closing the digital divide, in particular, for areas in which fixed telephony is not available. The current mobile Internet situation confirms that the three main conditions proposed by Gunasekaran and Harmantzis (2007), which are accessibility, availability, and affordability of service and applications, are applicable to Thailand. There are therefore several regulations and policies that the regulator and policymakers could consider to bridge the digital divide in the country. In terms of accessibility and availability, the wireless infrastructure, in particular Wi-Fi hotspots, only provides full coverage in populated areas. People in other parts of the country where fixed line infrastructure is not available can therefore only access the Internet if they have a mobile Internet device, i.e., Smartphone, and wireless infrastructure is available. The current 3G service offerings are very limited, however, due to the legal difficulties of allocating the new 3G frequencies. This has unfortunately resulted in EDGE currently being the best option for nationwide coverage. In terms of affordability, the results show that the price of mobile Internet service is inelastic. In terms of speed and price, the speed of wireless Internet connection is 2–3 times higher than that of dial-up Internet connection, while the price of mobile Internet is about the same as that of dial-up. The average monthly payment is 458 THB or 3.82% of the individual monthly income, which is cheaper than an ADSL connection. From the results, mobile Internet can play an important role in bridging the digital divide in Thailand in the short term. There is a weak response to price change of mobile Internet service however. This suggests that the mobile Internet user has a limited choice. The demand for Internet has grown rapidly since 2001, but there is an issue of concession that creates some difficulties for the Internet service providers to offer fixed Internet connection (Srinuan, 2011). The regulator therefore needs to promote fiercer competition in the environment in order to create more options and drive the price down. In the long term, the quality of service of mobile Internet needs to be announced explicitly by the regulator and ensure that consumers gain the benefits. 4.2.2. Implications for business Even though the results show that content and applications, i.e., email and social network applications, may not be the determinant factors for mobile Internet, many applications are still being developed and provided on the Internet today. As mobile Internet grows in popularity, the content and applications become the focus of many players in the value chain, including mobile service providers. An application can be free because the developer is offering it at no cost to the consumer while charging for other things within the application, e.g., advertising. Free application and content development may encourage consumers to have more experience of mobile Internet. Nevertheless, language could be a barrier for Thai consumers to use mobile Internet. This suggests that there are still opportunities for the industry to catch this group of users by developing applications that serve consumer needs. These issues need to be investigated further however. 5. Conclusion The transition from fixed to wireless technology is now taking over not only in voice communication but also in data communication and on the Internet. This phenomenon may be a good sign for bridging the digital divide by means of wireless technology. However, it is difficult to develop policies to accelerate the diffusion of mobile Internet without understanding the underlying factors that explain mobile Internet adoption. This study has examined mobile Internet adoption in Thailand based on a nationwide survey of individuals in 2010. The result of the logit regression model shows that price of mobile Internet service, availability of fixed telephony, age of user, living area, and mobile operator are the important drivers for mobile Internet usage and adoption. Specifically, the results indicate that price of mobile service is inelastic and mobile Internet could be a potential means to bridge the digital divide in areas that lack fixed line facilities. The results also provide a better understanding of application attributes, as useful and easy-to-use applications could encourage people to connect to the Internet via mobile phone. In the case of Thailand, the application is still at an early stage and has not influenced mobile Internet adoption as expected. Moreover, these results suggest that the NRA needs to stimulate the growth of mobile Internet. In the short term, frequency allocation will be an urgent task for the NRA in order to serve consumer needs. In parallel, the NRA needs to consider how to encourage investment in fixed and wireless infrastructure to eliminate the limitation of bandwidth capacity in the long run. To make the analysis more dynamic, future research could make a distinction between traditional mobile users and
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mobile Internet users with a longer period of study. The results would then be useful for regulators and policymakers to analyze mobile Internet adoption. Acknowledgments The authors would like to thank the National Telecommunications Commission of Thailand (NTC) for providing valuable data support, as well as the survey team at the Thammasat University, and Professor Masatsugu Tsuji and Ms. Orada Teppayayon of for their helpful comments. The early version of this paper was presented in the 2011 ITS Asia-Pacific Regional Conferences, 26–28 June, 2011 Taiwan. References Ahn, H., Lee, M., 1999. An econometric analysis of the demand for access to mobile telephone networks. Information Economics and Policy 11 (3), 297–305. Advance Info Service (AIS), 2003. AIS Annual Report. Retrieved 20 February 2001 from