Smart phone demand: An empirical study on the relationships between phone handset, Internet access and mobile services

Telematics and Informatics 32 (2015) 158–168

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Telematics and Informatics journal homepage: www.elsevier.com/locate/tele

Smart phone demand: An empirical study on the relationships between phone handset, Internet access and mobile services Ming-Hsiung Hsiao ⇑, Liang-Chun Chen Department of Information Management, Shu-Te University, 59 Hun Shan Rd., Yen Chau, Kaohsiung 824, Taiwan

a r t i c l e

i n f o

Article history: Received 15 October 2013 Accepted 2 June 2014 Available online 19 June 2014 Keywords: Mobile phone Smart phone demand Mobile Internet Mobile service Value-added service

a b s t r a c t This study clearly identified the differences among different dimensions of users’ demand for mobile/smart phone; i.e., the smart phone handset, subscription of mobile network and usage of mobile services such as voice calls and SMS or some value added services such as GPS via the mobile Internet, and then examined the relationship between these three demand dimensions, and the effect of users’ demographic characteristics on the dimensions as well, by an empirical study in Taiwan. The results show that the usage of the mobile services are not significantly affected by how consumers choose smart phone handset, and how they subscribe to the mobile network. For the effect of users’ demographic characteristics, gender’s effect is observed, but is not as obvious as what have been pointed out in the literature. The most noticeable relationship is the effect of demographic variables on the attributes of mobile network. Specifically, users’ gender, age, occupation and income are found to have significant effects on the contract with voice and 3G Internet, and the monthly 3G Internet fee. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction According to the online PC Magazine Encyclopedia (http://www.pcmag.com/encyclopedia/), a smart phone is defined as ‘‘a cellular telephone with built-in applications and Internet access. In addition to digital voice service, modern smart phones provide text messaging, e-mail, Web browsing, still and video cameras, MP3 player and video playback and calling.’’ Among others, the Internet access is without doubt the most important feature for a mobile phone to be called a smart phone. Many of the built-in applications of a mobile phone; e.g., e-mail, Web browsing, rely on its Internet access via Wi-Fi or 3G network. For a consumer to actually use a mobile or a smart phone, three elements are indispensable: (1) a mobile or smart phone handset, (2) a subscription to the 2G/3G network, and (3) mobile services such as voice calls, SMS (short message services) or some value added services such as GPS via the mobile Internet. This is to say that to enjoy the services provided by the use of a mobile/smart phone, a consumer has to pay for all these three kinds of product and/or services, even though what he/she really needs is actually the third one only; i.e., mobile services, in most cases. In economics terms, the demand for a mobile/smart phone handset and subscription to the 2G/3G network, much like the travel demand (Mokhtariana and Salomon, 2001; Mokhtariana et al., 2001), is called the ‘derived demand’, derived from the direct demand for the mobile service, which yield direct satisfaction to the consumers. To examine consumers’ demand for a mobile/smart phone, an overall perspective from the direct and indirect, which covers all these three different elements or dimensions, is needed. In the literature which tried to examine the demand for mobile/smart phone, however, none is found ⇑ Corresponding author. Tel.: +886 7 6158000x3017; fax: +886 7 6158000x3099. E-mail addresses: [email protected] (M.-H. Hsiao), [email protected] (L.-C. Chen). http://dx.doi.org/10.1016/j.tele.2014.06.001 0736-5853/Ó 2014 Elsevier Ltd. All rights reserved.

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to have clearly identified these different demand dimensions and have given an overall view covering the above three. Most studies have been focused on examining a single dimension of mobile/smart demand, including topics in a wide range from micro to macro perspective, from IT to commerce, and from individual behavior to global market analysis. For example, some studies adopted macro perspective to examine the market of mobile/smart phone handsets and made international comparisons and some examine the penetration rate of 2G/3G subscription demand, mostly from a macro view also. Among others, studies on the mobile services from a micro perspective maybe the most diversified. Take the m-commerce for example, in their review of 149 m-commerce articles, Ngai and Gunasekaran (2007) grouped the themes into five distinct categories: (1) m-commerce theory and research, (2) wireless network infrastructure, (3) mobile middleware, (4) wireless user infrastructure, and (5) m-commerce applications and cases. Category (2–4) is the research related to IT, while category (1) and (5) related to the behavioral or social science and most of them are related to commerce. Recently, more and more studies examine the mobile/smart phone use behavior of individual consumers by aiming at their perception of mobile/smart phone attributes. For example, Leung and Wei (2000) found that affection/sociability, entertainment, instrumentality, psychological reassurance, fashion/status, mobility and immediate access are the major factors driving consumers to use mobile phones. On the other hand, Leung (2007) found that sociability, instrumentality, reassurance, entertainment, acquisition and time management are the critical factors. In addition, he also defined six categories of gratifications of the SMS use, namely, entertainment, affection, fashion, escape, convenient and low cost as well as coordination. Although the literature on mobile/smart phone use is abundant, they do not seem to have given an overall view on consumers’ demand covering the three aforementioned elements. The purpose of this study, therefore, is to examine the individual consumers’ demand for smart phone in Taiwan from the three dimensions described above and examine the relationship between them. We hypothesize that there are dependent relationship between the direct demand; i.e., mobile services, and the derived demand; i.e., mobile/smart phone handset and subscription to the 2G/3G network. In addition, we also consider the effect of consumers’ socio-demographic characteristics on these direct and derived demands in this study. The remainder of the paper is organized as follows. In the next section, it discusses the three dimensions of smart phone demand by reviewing some relevant literature. It is followed by a section describing the research framework and the data collection on the smart phone demand in Taiwan for empirical analysis. In section four, it shows the data analysis results. Finally, it gives a summary and draws the conclusions in section five.

2. Dimensions of mobile/smart phone demand 2.1. Mobile/smart phone handset Mobile phone handsets have a high turnover rate. Their potential life span is approximately ten years, but most users change their phones frequently, causing the usable life of these devices to decrease to 12–24 months (Paiano et al., 2013). The characteristic of the mobile/smart phone handset industry is multi-faceted; e.g., rapidly evolving nature with short product life-cycles (Tseng and Lo, 2011), the addition of new features to the mobile phones, and fierce competition among numerous companies in the industry (Haverila, 2011). According to Dedrick et al. (2011), though the carriers capture the greatest share of gross profits from each phone handset, followed closely by the handset makers, the latter actually are able to retain more of that profit than the carriers, and capture far greater value than any of their component suppliers. This in turn enables handset makers to upgrade their product in a quick way to attract consumers and possibly make the life span of mobile phone shorter. Today a smart phone is a device served as a mobile computer. Even without connection to the network or the Internet, consumers can use a smart phone to engage in a variety of activities such as taking photos by built-in camera, scheduling by calendar, listening to music by mp3 player. Among others, camera seems to be the most noticeable and useful built-in attribute in daily life and has drawn some authors’ attention for research. For example, Rouibah and Abbas (2010) applied a qualitative field study to build their research model and then a quantitative field study to examine the acceptance and usage of camera mobile phones, and Rouibah et al. (2011) investigated the factors affecting camera mobile phone adoption before consumers’ e-shopping in the Arab world by using the second technology acceptance model (TAM2). Apart from built-in camera, some other features have also been noted in the literature. In their empirical study on examining gender differences regarding the importance and costs of mobile devices’ characteristics, for example, Economides and Grousopoulou (2009) found that students tend to consider the following features important: battery life, mp3 player, video camera, photo camera, storage memory, Bluetooth, design and elegance, clock, calendar, organizer and reminder, while most of the respondents in their study do not consider the following important: touch screen, voice commands, chat, teleconference, encryption and cryptography, common use of files, printing. Mobile phone manufacturers like to add features to the phone because they can help enhance and differentiate their products from competitors with low marginal cost (Head and Ziolkowski, 2012). Consumers also seem to enjoy these new additional features at a low cost. However, as Head and Ziolkowski (2012) pointed out, greater product feature complexity would require greater consumer effort and that consumers naturally wish to minimize their decision efforts. Too many features may make a product overwhelming, thus lead to consumer dissatisfaction. Isßıklar and Büyüközkan (2007) proposed a multi-criteria decision making approach in assessing mobile phones as regards to the user’s feature preferences order in Turkey. In their study, Isßıklar and Büyüközkan gathered the criteria for selecting a

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mobile phone into two main categories: product-related criteria and user-related criteria. The product-related criteria then cover three sub-criteria: (1) the basic requirements, such as the cost/price of the phone, the standard part used or the standard process applied; (2) the physical characteristics of the phone involving its design standards such as its weight, dimension and shape features, water resistance, solidity, attractiveness or the raw material properties; (3) the technical features including talk and standby time, international roaming or safety standards in terms of radiation. Following the criteria proposed by Isßıklar and Büyüközkan (2007), Haverila (2011) investigate the mobile phone feature preferences and their relationship with customer satisfaction and repurchase intent among the young male users in Finland. The study found that the most frequent used features, in sequence, are: phone, SMS, Internet, calendar, music, e-mail, camera + video, calculator, notes, other, games, pictures, MMS. Haverila (2011) also noted that when the customer faces additional functionality and enlarged complexity in the mobile phone, he/she is likely to encounter problems, which can have a negative impact on the customer satisfaction, and consequently decrease customers’ repurchase intent.

2.2. Subscription of mobile phone network Most of the studies discussing subscription of mobile network are from a macro perspective. We often call it the mobile phone ‘penetration rate’, which is the number of active mobile phone lines within a specific population. According to ITU (2013), as of 2013, the world average penetration rate is estimated 96.2%, nearly the triple of that in 2005. Even the Africa, the region with the lowest penetration rate, has a number of 63.5%. In Taiwan, the mobile phone penetration rate is 115% in 2003, which was one of the highest countries in the world in that year. In 2011, this number reached about 124%, still ranking high worldwide. As of 2008, the world active mobile-broadband subscriptions per 100 inhabitants surpassed that of the fixed-broadband. In 2013 the world active mobile-broadband penetration rate reached 29.5%, which is far higher than that of the fixed-broadband, 10% (ITU, 2013). In the same period of time, the penetration rate of the fixed line has dropped slowly, from 19.1% in 2005 to 16.5% in 2013 (ITU, 2013). Actually the relationship between different media, mobile or fixed lines for example, has long been a disputable issue. Ahn and Lee (1999) conducted a cross-national modeling of mobile services and found a complementary relationship between mobile and fixed telephones. However, Madden and Coble-Neal (2004) found a substitution effect between mobile and fixed telephones by using a dynamic panel data approach. Lin et al. (2013) also reported that some scholars found a decreased use of older media with the introduction of the Internet, whereas others discovered complementary relationships between new and old media. Akematsu et al. (2012) claimed that there are two stages in the diffusion of the Japanese 3G. The first stage is technological, involving the technology-related characteristics, which enable the provision of more value to a large number of subscribers, while the second stage involves the development of 3G mobile services. With regard to the Japanese mobile market, Iimi (2005) explored the demand for mobile phone services and examine the influence of basic 2G services, including voice mail and specific discount packages. Ida and Kuroda (2009) explored the demand for mobile phone services in Japan by comparing both 2G and 3G mobile phones to examine whether 3G was a substitute for 2G, and concluded that in the case of NTT DOCOMO 3G was a substitute for 2G, while the 3G of other carriers was not. The substitution effect between mobile and fixed telephones may bring about socio-economic reform in a nation. In a study which examined the distributional effects of leapfrogging in mobile phones, James (2012) concluded that in lowincome countries the relative lack of fixed-line phones possibly facilitates the growth of the mobile alternatives, which may accordingly give low-income developing countries and households more welfare benefits from a given number of mobile phones than those countries and households that are better off.

Table 1 Sample profile.

*

Profile

Item (scale)

Percent (%)

Gender

Male Female

42.73 57.27

Mean

SD

Education

High school and below College/University Graduate and above

17.75 59.17 23.08

Occupation

Student Work for private company Other

28.49 36.19 35.31

Job position*

Managerial level General staff

16.82 83.19

Age Monthly income

(Years)

28.8

7.60

(USD)

895.4

735.4

Exclusive of the student, housewife and unemployed sample.

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2.3. Mobile services Voice call and SMS are doubtlessly the two kinds of mobile services which are the most widely used by consumers. How to measure their demand, however, has been an issue. In an empirical study examining the psychological characteristics, mobile phone addiction and use of mobile phones in Taiwan, Hong et al. (2012) used the number of calls per day and daily number of text messages sent to measure consumers’ mobile phone usage behavior. Walsh et al. (2011) used the average number of calls made, calls received, text messages read, and text messages received each day to indicate the frequency of mobile phone use. Moreover, In an empirical study on the demand for SMS and mobile voice telephony at the individual customer level in Germany, Gerpott (2010) used outgoing and incoming voice traffic (minutes per month), and outgoing and incoming SMS traffic (number per month) to measure users’ demand for voice call and SMS. Apart from the demand for voice call and SMS, a number of past studies have focused on different services that mobile devices offer. For example, Venkatesh et al. (2003) studied the profiles of mobile users and the patterns for their use of different mobile services, and Batool and Asghar (2012) examined the mobile phone text messaging use among university librarians in Pakistan. In their study on adolescents’ mobile phone use, Lin et al. (2013) investigated 14 kinds of activities on the mobile phone including making phone calls via the mobile phone. The 14 activities were under three groups: (1) task-based activities, covering as school/research, TV, shopping, news, vote and sign petition; (2) recreation-based activities, covering message/chat, playing games, music and telephony; (3) information and communication, covering email, seeking information, BBS and blogging. They also found that the adolescents tend to use the mobile phone for recreation and entertainment purposes, especially playing games and listening to music and are less likely to use the mobile phone for more sophisticated purposes such as petitioning, voting, or shopping. In addition to the use frequencies of mobile services, some other authors concerned about the psychological aspect in using mobile phones such as text-messaging dependency or addiction; e.g., Masataka (2005), Yellowlees and Marks (2007) and Lu et al. (2011). Rouibah (2008) used subjective norms, curiosity about other people, and perceived usefulness to predict the social usage of instant messaging, including the daily usage volume (an estimate of total time spent per day using instant messaging) and frequency of use (how many times per week the respondent reports using instant messaging), in Kuwait. Moreover, Bouwman et al. (2012) evaluated as many as 48 mobile services by an exploratory approach. Another issue is regarding the personal demographic factors’ effect on mobile phone use. Potongsangarun et al. (2012) applied Probit and Multivariate Probit Models to find out factors influencing consumers’ attitude toward mobile phone of all aspects, including identity, usage, and expenses aspect. The factors they found included personal characteristics such as gender and age, behavior of using mobile phone, and marketing mixed of mobile phone network. Actually, literature has shown that gender and age are the two most important demographic variables which have effects on mobile/smart phone use, and young adults especially males tended to use a mobile phone while driving more than older drivers or female even though the result is still inconclusive (Isa et al., 2012). The difference between male and female regarding the mobile phone use behavior has especially been noticeable. For example, Baron and Campbell (2012) found out a number of gendered usage and attitudinal patterns towards mobile phone usage. Ono and Zavodny (2005) have also found that males were more likely than females to utilize information and communication technologies (ICTs). In the literature review, Hislop (2012) reported that male drivers were more likely to use mobile phones while driving than female ones. Moreover, Economides and Grousopoulou (2009) developed a survey to examine gender differences among students regarding the importance and costs of mobile devices’ characteristics and found that though some differences so exist. Haverila (2011) even focused his study on male users, investigating the mobile phone feature preferences among male respondents in Finland. Apart from gender’s effect, in a study investigating student attitudes of mobile phone features, Head and Ziolkowski (2012) reported that university/college students have been labeled as one of the most important target markets and the largest consumer group of mobile phone services. For this special group of users, researchers have explored multiple facets of mobile phone use, including motivation, psychological and health effects, etiquette, implications on social networks, impact on campus life (Head and Ziolkowski, 2012). Balakrishnan and Raj (2012) also reported that the youth considers style important when purchasing a mobile phone and perceive mobile phones as a fashion accessory. In their study empirical study on local university students in Malaysia, Balakrishnan and Raj (2012) found out that the respondents consider brand, trend and price being the three most important purchasing factors while socializing and privacy emerged as the two most important reasons to use mobile phones. Ansari et al. (2012) investigated the mobile phone adoption and appropriation among the young generation based on the intrinsic and extrinsic motivations theory and the adoption/appropriation of technology perspective.

3. Methodology 3.1. Theoretical framework Based on the three dimensions described above; i.e., smart phone handset, subscription of mobile network and usage of mobile services, this study builds a research framework to examine the relationship between the different dimensions of demand for smart phone, as illustrated in Fig. 1. In this research framework, the mobile services are considered the

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Mobile Network

Demographic Variables

Value-Added Services

Smart Phone Handset Fig. 1. Research framework.

dependent variable, which is affected by the other two dimensions, attributes of smart phone handset and the mobile network subscription. Moreover, to lay our emphasis on the smart phone feature, the mobile services are referred specifically to the value-added services provided over the mobile Internet. The users’ demographic characteristics, such as gender, age and income, are considered the independent variables having effects on all the three demand dimensions. As mentioned earlier, many of the users’ demographic characteristics, gender and age in particular, have been verified to have significant impact on the mobile phone use. We intend to test these effects by the empirical study in Taiwan 3.2. Questionnaire design and survey This study adopts a micro view to investigate individual consumers’ demand for smart phone. Therefore each variable is measured according to individual users’ report about how they choose and use the product and/or the services related to the smart phone in use. This study used questionnaire survey to collect the data needed. The questionnaire included four parts of questions: (1) (2) (3) (4)

How users subscribed to their mobile network. How users chose their smart phone handsets. How users used the value-added service via mobile Internet. Users’ socio-demographic characteristics.

The questionnaire was delivered to those who have smart phones in use. The survey was conducted during May, 2012, lasting for two weeks. Among 345 questionnaires delivered, 338 of them were collected. After discarding 42 questionnaires which had incomplete answers, we finally collected 296 effective questionnaires. 4. Results 4.1. Sample description Table 1 shows the profile of our sample. Most of them have a college or university degree with an average age of nearly 29 years old and with an average monthly income of USD895.4. 83.19% of the respondents who have a paid job working as general staff. Taiwan’s mobile telecommunications market is an oligopoly, with a leader, Chunghwa Telecom (CHT) and two primary followers, Taiwan Cellular Corp. (TCC) and Far Eastone Telecom (FET). The percentages shown in Table 2 reflect this fact in terms of the telecom carrier the respondents subscribe for, where the shares of all these three carriers account for more than 94%. Moreover, as high as 66.77% of the respondents have contract with voice and 3G Internet, where 79.88% of them access the Internet via 3G network with an average monthly 3G Internet fee of USD36.0. For the mobile/smart phone handset attributes, Table 3 shows their descriptive statistics. Apple iPhone and HTC are the two brands dominate the share, with a combined percentage of 65.38%. Android, as expected, has the highest share, 61.38%, in the operation system. Though single CPU core still dominates the market, dual and even more cores still has a high share of 45.32%. From the mean and SD of other attributes shown in Table 3; e.g., camera, screen size, memory size and CPU speed, it can be seen that our sample reflect well to today’s smart phone attributes in Taiwan in terms of their mean values. Even the average price of a smart phone in our sample is USD352.7, is also a very reasonable one. Table 3 also shows the mean and SD of the appearance attributes which have attracted consumers to purchase it on the 7-point Likert scale. The table shows that design, color and material are the three most important attributes with average points above five on a 7-point scale.

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M.-H. Hsiao, L.-C. Chen / Telematics and Informatics 32 (2015) 158–168 Table 2 Descriptive statistics for mobile network. Variable

Item

Percent (%)

Carrier

Chunghwa Telecom (CHT) Taiwan Cellular Corp. (TCC) Far Eastone Telecom (FET) Other

37.28 26.92 29.88 5.92

Contract

Contract with voice and 3G Internet Contract with voice only No contract and other

66.77 13.35 19.88

How to access to the Internet (Internet access)

3G Wi-Fi

79.88 20.12

Years of experience in using mobile Internet (experience)

Under 1 year 1–2 Years Above 2 years

44.35 44.64 11.02

Monthly 3G Internet fee (USD)

Mean

SD

36.0

15.9

Table 3 Descriptive statistics for smart phone handset attributes. Variable

Item

Percent (%)

Brand

Apple iPhone Sony Samsung HTC Other

27.81 9.76 15.98 37.57 8.88

Mean

SD

Operation System

Apple IOS Android Other

28.74 61.38 9.89

CPU core

Single Dual and above

54.68 45.32

Camera Screen size Weight Memory size CPU speed Price

(M. Pixel) (Inch) (Gram) (GB) (MHz) (USD)

654.35 3.865 129.46 11.10 981.60 352.7

214.19 0.506 18.45 11.90 344.76 251.8

Appearance attracted when purchasing (7-point Likert scale)

Color Material Design Battery Accessory Gift

5.10 5.12 5.67 4.69 4.13 3.79

1.546 1.462 1.251 1.556 1.429 1.575

Table 4 shows the frequencies on how consumers use the value-added services via the mobile Internet. This study uses the 7-point Likert scale to measure the usage frequencies of news/weather forecast, map/traffic, GPS, dictionary, and antivirus because they are not easy for the users to define. It is obvious that the map/traffic and GPS are the two types of services that consumers use most often. For other usage types, we use frequency to describe uses’ use behavior. Table 4 shows that SMS is the most frequent one that consumers use by APP such as LINE. On average, our respondents make 12.37 SMS per day, which is far more than the other types such as MMS and voice call. For entertainment purpose, game download is the most frequent one consumers use, followed by the video/movie download. For the personal communications, visiting community websites such as Facebook and checking email are the two types which consumers use frequently.

4.2. Effect of demographic variables on smart phone demand Table 5 shows the test results for the effects of demographic variables on smart phone demand, including that for mobile network, smart phone handset and value-added services. The results show that the demographic variables seem to have effects on mobile network more extensive than on smart phone handset and value-added services. All the attributes of mobile network are affected to an extent by certain demographic variables. Among others, 3G Internet fee and contract, the two attributes associated with monetary expenses, are widely affected by demographic variables, especially by gender, occupation, age and income.

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Table 4 Descriptive statistics for value-added service usage via mobile Internet. Attribute

Definition/measurement (scale)

Mean

SD

News/weather forecast Map/traffic GPS Dictionary Antivirus Voice call SMS MMS Game Music/ring tone Video/movie TV APP download Ticketing/reservation Payment Banking Email Online chatting Community website

How often browsing news, weather forecast, etc. (7-point Likert scale) How often checking the map, traffic condition, etc. (7-point Likert scale) How often using GPS (7-point Likert scale) How often using online dictionary (7-point Likert scale) How often using antivirus for mobile phones (7-point Likert scale) Number of calls made per day using APP such as LINE Number of SMS made per day using APP such as LINE Number of MMS made per day using APP such as LINE Frequency of games downloaded per week Frequency of music/ring tone downloaded per week Frequency of video/movie tone downloaded per week Frequency of watching online TV per week Frequency of APP downloaded per week Frequency of making ticketing/reservation per week Frequency of making payment per week Frequency of using banking per week Frequency of checking email per day Frequency of using online chatting per day Frequency of visiting community websites such as FB per day

4.82 5.27 5.07 4.62 3.49 2.61 12.37 5.25 2.55 1.25 2.08 1.73 2.31 0.72 0.22 0.62 2.35 1.96 3.58

1.56 1.47 1.51 1.51 1.60 4.96 27.83 14.70 4.55 3.36 6.49 3.76 4.18 1.28 0.62 1.52 4.75 3.44 4.44

As discussed earlier, many authors have pointed out the significant difference between male and female mobile phone users. After a closer look at the relative ratio, it is interesting to find that female users tend to have contract with voice and 3G Internet significantly higher than males ones, while male users use 3G to get access to the mobile Internet far more than female ones. Moreover, male users pay the monthly 3G Internet fee also higher than female ones. In the occupation, students’ behavior is especially worth noting in that they tend to, compared to other occupation, choose Chunghwa Telecom (CHT), use 3G to get access to the mobile Internet and pay lower monthly 3G Internet fee. For the age effect, low-age group is found to have contract with voice and 3G Internet and pay lower monthly 3G Internet fee. For the income effect, low-income group is found to choose Chunghwa Telecom (CHT), use Wi-Fi to get access to the mobile Internet and pay lower monthly 3G Internet fee, and have no contract with voice and 3G Internet. On the other hand, the effects of demographic variables on smart phone demand for handset and value-added services are not extensive and explicit. None of the demographic variables demonstrate extensive and explicit effect on any specific handset attributes and value-added services, and none of the handset attributes and value-added services are affected extensively and explicitly by any specific demographic variables. What many authors concerned in the literature; e.g., gender’s effect, demand for voice call and SMS, does not show the corresponding results in our empirical study. One worth noting is the use of community website such as Facebook, which is affected by gender, job position, age and income. It is found that female, general staff, low-age and low-income groups tend to use community website such as Facebook more than other groups. 4.3. Network characteristics on mobile service demand Table 6 shows the test results for the effect of mobile network attributes on mobile service demand value-added services. Internet access and 3G fee seem to be the two having the most extensive effects on mobile network attributes. Specifically, map/traffic, GPS, dictionary, SMS, game, ticketing/reservation email and community website depend significantly on how they access to the Internet (Wi-Fi or 3G), while those of news/weather forecast, map/traffic, GPS, dictionary, antivirus, TV, payment and banking depend significantly on 3G fee. On the other hand, consumers’ uses of news/weather forecast, map/ traffic, GPS, and dictionary receive the widest impact of mobile network attributes, including contract, Internet access, mobile Internet experience and 3G fee. This seems reasonable because most of these value-added services require Internet access and 3G fee. News/weather forecast, map/traffic and GPS, in particular, require mobile Internet to get up-to-date information. 4.4. Effect of handset characteristics on mobile service demand Table 7 shows the test results for the effect of handset attributes on the demand for value-added services. It is obvious that map/traffic and GPS receive the widest impact of mobile handset attributes, including brand, OS, CPU core, camera, screen and memory. On the other hand, brand, OS, and memory seem to have obvious effects on the demand for value-added services. Such results are reasonable because when users try to get access to the information on map/traffic and GPS by 3G or WiFi, they generally need a stronger CPU to speed up the processing, a bigger screen to view the information and larger memory to store the information, especially some images downloaded or photos taken. To take a closer look at the group mean, it is found users who use Apple mobile phone with its special iOS tend to use news/weather forecast, map/traffic and GPS more

165

M.-H. Hsiao, L.-C. Chen / Telematics and Informatics 32 (2015) 158–168 Table 5 Test for the effect of demographic variables on smart phone demand. Demand type

Attribute

Gender

Education

Occupation

Job position

Age

Income

Network

Carrier

v2 = 3.889 (0.274) v2 = 9.439 (0.009) v2 = 7.614 (0.006) v2 = 13.279 (0.001) F = 11.155 (0.001)

v2 = 38.753 (0.000) v2 = 0.278 (0.991) v2 = 0.057 (0.972) v2 = 4.309 (0.366) F = 1.177 (0.309)

v2 = 16.526

v2 = 1.790 (0.617) v2 = 1.418 (0.492) v2 = 1.772 (0.183) v2 = 18.207 (0.000) F = 5.322 (0.022)

v2 = 5.987 (0.425) v2 = 11.657 (0.020) v2 = 0.382 (0.826) v2 = 19.859 (0.001) r = 0.209 (0.000)

v2 = 23.961

v2 = 7.911 (0.095) v2 = 4.628 (0.099) v2 = 0.122 (0.727) F = 1.441 (0.231) F = 2.528 (0.113) F = 2.741 (0.099) F = 1.584 (0.209) F = 0.587 (0.444) F = 1.906 (0.168) F = 0.010 (0.919) F = 1.719 (0.191) F = 8.069 (0.005) F = 8.072 (0.005) F = 1.640 (0.201) F = 0.001 (0.979)

v2 = 13.080 (0.109) v2 = 14.860 (0.005) v2 = 2.067 (0.356) F = 0.133 (0.875) F = 0.284 (0.753) F = 0.693 (0.501) F = 2.828 (0.061) F = 1.346 (0.262) F = 2.201 (0.112) F = 0.625 (0.536) F = 0.462 (0.630) F = 1.219 (0.297) F = 2.806 (0.062) F = 4.239 (0.015) F = 12.733 (0.000)

v2 = 12.458 (0.132) v2 = 6.102 (0.192) v2 = 7.584 (0.023) F = 1.221 (0.296) F = 0.910 (0.403) F = 2.305 (0.101) F = 1.607 (0.202) F = 2.840 (0.060) F = 1.034 (0.357) F = 0.316 (0.729) F = 1.296 (0.275) F = 0.978 (0.377) F = 2.782 (0.063) F = 5.536 (0.004) F = 6.099 (0.003)

v2 = 6.995 (0.136) v2 = 7.167 (0.028) v2 = 0.691 (0.406) F = 0.064 (0.800) F = 0.263 (0.608) F = 2.359 (0.126) F = 1.097 (0.296) F = 0.038 (0.846) F = 0.279 (0.598) F = 0.240 (0.625) F = 0.077 (0.782) F = 1.057 (0.305) F = 0.159 (0.690) F = 0.107 (0.744) F = 1.026 (0.312)

v2 = 11.520 (0.174) v2 = 4.143 (0.387) v2 = 1.431 (0.489) r = 0.030 (0.582) r = 0.018 (0.741) r = 0.057 (0.305) r = 0.048 (0.387) r = 0.024 (0.667) r = 0.097 (0.083) r = 0.106 (0.052) r = 0.026 (0.631) r = 0.025 (0.655) r = 0.057 (0.305) r = 0.106 (0.054) r = 0.009 (0.871)

(0.014) r = 0.034 (0.538) r = 0.006 (0.917) r = 0.030 (0.592) r = 0.088 (0.112) r = 0.006 (0.912) r = 0.035 (0.536) r = 0.014 (0.801) r = 0.086 (0.117) r = 0.056 (0.315) r = 0.098 (0.076) r = 0.128 (0.020) r = 0.124 (0.024)

F = 12.059 (0.001) F = 25.271 (0.000) F = 28.950 (0.000) F = 0.560 (0.455) F = 2.542 (0.112) F = 0.189 (0.664) F = 0.292 (0.589) F = 0.011 (0.917) F = 7.365 (0.007) F = 0.146 (0.703) F = 0.825 (0.364) F = 0.206 (0.650) F = 0.873 (0.351)

F = 1.772 (0.172) F = 0.479 (0.620) F = 0.017 (0.983) F = 3.864 (0.022) F = 8.113 (0.000) F = 2.139 (0.119) F = 4.960 (0.008) F = 3.638 (0.027) F = 1.093 (0.337) F = 0.403 (0.669) F = 0.181 (0.835) F = 0.154 (0.858) F = 3.935 (0.020)

F = 0.659 (0.518) F = 4.379 (0.013) F = 4.583 (0.011) F = 7.269 (0.001) F = 3.228 (0.041) F = 1.454 (0.235) F = 1.937 (0.146) F = 1.219 (0.297) F = 1.687 (0.187) F = 0.063 (0.939) F = 0.341 (0.711) F = 2.079 (0.127) F = 0.332 (0.718)

F = 5.816 (0.017) F = 1.455 (0.229) F = 0.267 (0.606) F = 3.038 (0.083) F = 0.023 (0.880) F = 0.259 (0.612) F = 0.152 (0.697) F = 0.010 (0.919) F = 0.078 (0.780) F = 3.560 (0.060) F = 0.772 (0.381) F = 0.204 (0.652) F = 0.001 (0.972)

r = 0.147 (0.007) r = 0.140 (0.011) r = 0.095 (0.085) r = 0.138 (0.012) r = 0.062 (0.259) r = 0.018 (0.743) r = 0.102 (0.067) r = 0.071 (0.203) r = 0.083 (0.129) r = 0.010 (0.856) r = 0.026 (0.640) r = 0.010 (0.859) r = 0.045 (0.415)

r = 0.064 (0.244) r = 0.068 (0.220) r = 0.061 (0.268) r = 0.122 (0.027) r = 0.011 (0.836) r = 0.012 (0.830) r = 0.050 (0.367) r = 0.021 (0.709) r = 0.032 (0.559) r = 0.023 (0.672) r = 0.031 (0.574) r = 0.056 (0.312) r = 0.026 (0.640)

Contract Internet access M-Internet experience 3G Internet fee Handset

Brand Operation System CPU core Camera Screen Weight Memory CPU speed Price Color Material Appearance

Design Battery Accessory Gift

Service

News/weather forecast Map/traffic GPS Dictionary Antivirus Voice call SMS MMS Game Music/ring tone Video/movie TV Ticketing/reservation

(0.011) v2 = 10.169 (0.038) v2 = 1.254 (0.534) v2 = 4.546 (0.337) F = 7.409 (0.001)

(0.021)

v2 = 20.665 (0.008)

v2 = 18.757 (0.001)

v2 = 15.221 (0.055) r = 0.243 (0.000)

v2 = 21.012 (0.178)

v2 = 10.179 (0.253)

v2 = 12.511

(continued on next page)

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Table 5 (continued) Demand type

Attribute

Gender

Education

Occupation

Job position

Age

Income

Payment

F = 0.587 (0.444) F = 0.519 (0.472) F = 0.703 (0.402) F = 3.692 (0.056) F = 0.014 (0.906) F = 4.222 (0.041)

F = 0.965 (0.382) F = 0.886 (0.413) F = 1.251 (0.288) F = 2.767 (0.064) F = 0.038 (0.962) F = 1.030 (0.358)

F = 3.761 (0.024) F = 2.858 (0.059) F = 0.852 (0.427) F = 0.425 (0.654) F = 0.049 (0.952) F = 1.470 (0.231)

F = 0.192 (0.662) F = 1.040 (0.309) F = 0.733 (0.393) F = 6.477 (0.012) F = 0.506 (0.478) F = 3.953 (0.048)

r = 0.016 (0.777) r = 0.196 (0.000) r = 0.034 (0.541) r = 0.049 (0.378) r = 0.183 (0.001) r = 0.292 (0.000)

r = 0.010 (0.855) r = 0.168 (0.002) r = 0.004 (0.945) r = 0.039 (0.483) r = 0.094 (0.087) r = 0.186 (0.001)

Banking APP Email Online chatting Community website

Table 6 Test for the effect of network attributes on mobile service demand. Attribute

Carrier

News/weather forecast Map/traffic GPS Dictionary Antivirus Voice call SMS MMS Game Music/ring tone Video/movie TV Ticketing/reservation Payment Banking APP Email Online chatting Community website

F = 1.041 F = 0.852 F = 0.831 F = 0.959 F = 3.806 F = 0.544 F = 1.087 F = 0.476 F = 0.111 F = 0.660 F = 1.449 F = 1.651 F = 0.463 F = 0.520 F = 1.170 F = 0.692 F = 1.847 F = 0.265 F = 0.685

(0.374) (0.467) (0.477) (0.412) (0.010) (0.652) (0.355) (0.700) (0.954) (0.577) (0.228) (0.178) (0.708) (0.668) (0.321) (0.558) (0.139) (0.851) (0.562)

Contract

Internet access

M-Internet experience

3G fee

F = 6.166 (0.002) F = 11.106 (0.000) F = 12.173 (0.000) F = 4.007 (0.019) F = 0.434 (0.648) F = 0.754 (0.471) F = 1.326 (0.267) F = 0.662 (0.516) F = 2.336 (0.098) F = 0.431 (0.650) F = 1.768 (0.172) F = 1.064 (0.346) F = 1.031 (0.358) F = 2.803 (0.062) F = 3.747 (0.025) F = 0.174 (0.841) F = 1.269 (0.282) F = 1.873 (0.155) F = 0.911 (0.403)

F = 13.187 (0.000) F = 48.909 (0.000) F = 56.373 (0.000) F = 10.516 (0.001) F = 1.581 (0.210) F = 0.003 (0.954) F = 4.342 (0.038) F = 0.729 (0.394) F = 6.389 (0.012) F = 3.843 (0.051) F = 0.841 (0.360) F = 1.491 (0.223) F = 4.599 (0.033) F = 1.498 (0.222) F = 2.224 (0.137) F = 0.100 (0.753) F = 4.749 (0.030) F = 0.875 (0.350) F = 9.889 (0.002)

F = 3.624 F = 2.640 F = 5.100 F = 0.892 F = 2.346 F = 0.228 F = 0.128 F = 0.776 F = 2.846 F = 1.101 F = 0.463 F = 0.410 F = 0.434 F = 0.633 F = 0.630 F = 0.065 F = 1.988 F = 1.025 F = 0.071

r = 0.287 r = 0.341 r = 0.371 r = 0.233 r = 0.124 r = 0.087 r = 0.092 r = 0.053 r = 0.085 r = 0.050 r = 0.078 r = 0.150 r = 0.046 r = 0.155 r = 0.132 r = 0.007 r = 0.077 r = 0.029 r = 0.070

(0.028) (0.073) (0.007) (0.411) (0.097) (0.796) (0.880) (0.461) (0.059) (0.334) (0.630) (0.664) (0.649) (0.532) (0.533) (0.937) (0.139) (0.360) (0.932)

(0.000) (0.000) (0.000) (0.000) (0.030) (0.126) (0.111) (0.353) (0.136) (0.384) (0.172) (0.009) (0.425) (0.006) (0.021) (0.908) (0.178) (0.610) (0.223)

than other brand and/or other system. To use map/traffic and GPS, better CPU and camera, larger screen and memory are needed. 5. Discussion and conclusions This study explored the smart phone demand by emphasizing the differences between the three demand dimensions: (1) mobile or smart phone handset, (2) subscription to the 2G/3G network, and (3) mobile services, and then examined the relationship between them, and the effect of users’ demographic characteristics on these three dimensions as well, by an empirical study in Taiwan. Although the authors made effort to examine all the possible relationship and effect across all the attributes of mobile/smart phone handset and mobile services concerned, we did not intend to emphasize any findings with respect to any specific or individual attributes. Rather, we hope to draw some insightful conclusions by an overview on the empirical results. The empirical results show that the relationship between the three dimensions of demand for smart phone handset, subscription of mobile network and usage of mobile services is not very extensive and explicit. This implies that how consumers choose smart phone handset, and how they subscribe to the mobile network do not significantly affect the usage of the mobile services. Such results, however, are somewhat surprising because instinctively we would think that how consumers use their product/service depends much on how they acquire or purchase it. The reasons behind this maybe that most of the mobile services via the mobile Internet; e.g., map, game, email, community, etc., are free to use or can be used at a very low price. Specifically, the users can use most of the mobile services freely without much reference to how they subscribe to the mobile network, as long as they can connect their smart phone to the free Wi-Fi. This is especially true for nations such as Taiwan where free Wi-Fi is often available in many places such as offices, homes and restaurants, or in most public places such as bus/metro/train stations, government offices, and post offices. Such weak relationship between the mobile services and smart phone handset, and between mobile services and the subscription to the mobile network, on the other hand, may provide the rationale for researchers to solely examine the demand

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M.-H. Hsiao, L.-C. Chen / Telematics and Informatics 32 (2015) 158–168 Table 7 Test for the effect of handset attributes on mobile service demand. Services

Brand

OS

CPU core

Camera

Screen

Weight

Mem.

CPU speed

Price

News/weather forecast Map/traffic

F = 8.513 (0.000) F = 7.634 (0.000) F = 10.602 (0.000) F = 7.525 (0.000) F = 2.942 (0.021) F = 1.856 (0.118) F = 1.475 (0.210) F = 0.973 (0.422) F = 3.238 (0.013) F = 1.436 (0.222) F = 0.978 (0.419) F = 2.101 (0.080) F = 0.519 (0.722) F = 1.260 (0.285) F = 0.237 (0.918) F = 0.534 (0.711) F = 2.221 (0.066) F = 1.702 (0.149) F = 1.025 (0.394)

F = 9.380 (0.000) F = 13.465 (0.000) F = 18.149 (0.000) F = 10.719 (0.000) F = 0.743 (0.476) F = 4.035 (0.019) F = 2.887 (0.057) F = 0.795 (0.453) F = 4.346 (0.014) F = 0.004 (0.996) F = 0.892 (0.411) F = 0.365 (0.695) F = 2.082 (0.126) F = 2.107 (0.123) F = 0.971 (0.380) F = 0.899 (0.408) F = 2.059 (0.129) F = 1.065 (0.346) F = 0.509 (0.601)

F = 1.595 (0.208) F = 6.817 (0.009) F = 9.717 (0.002) F = 2.304 (0.130) F = 5.694 (0.018) F = 2.337 (0.127) F = 0.676 (0.412) F = 0.075 (0.784) F = 1.669 (0.197) F = 1.847 (0.175) F = 0.964 (0.327) F = 3.308 (0.070) F = 0.414 (0.520) F = 0.164 (0.686) F = 0.418 (0.518) F = 0.002 (0.968) F = 0.257 (0.612) F = 0.002 (0.966) F = 0.108 (0.742)

r = 0.063 (0.253) r = 0.182 (0.001) r = 0.171 (0.002) r = 0.028 (0.614) r = 0.130 (0.018) r = 0.066 (0.228) r = 0.023 (0.683) r = 0.019 (0.727) r = 0.091 (0.097) r = 0.105 (0.055) r = 0.053 (0.335) r = 0.097 (0.079) r = 0.030 (0.584) r = 0.016 (0.769) r = 0.055 (0.317) r = 0.065 (0.238) r = 0.033 (0.551) r = 0.045 (0.416) r = 0.007 (0.902)

r = 0.008 (0.885) r = 0.123 (0.024) r = 0.118 (0.031) r = 0.031 (0.573) r = 0.083 (0.127) r = 0.064 (0.241) r = 0.078 (0.159) r = 0.088 (0.113) r = 0.067 (0.221) r = 0.046 (0.399) r = 0.048 (0.383) r = 0.077 (0.162) r = 0.123 (0.025) r = 0.015 (0.778) r = 0.009 (0.876) r = 0.091 (0.097) r = 0.019 (0.723) r = 0.135 (0.013) r = 0.065 (0.236)

r = 0.063 (0.255) r = 0.069 (0.209) r = 0.088 (0.111) r = 0.023 (0.683) r = 0.002 (0.975) r = 0.025 (0.648) r = 0.078 (0.161) r = 0.058 (0.298) r = 0.097 (0.078) r = 0.086 (0.116) r = 0.027 (0.619) r = 0.021 (0.708) r = 0.135 (0.014) r = 0.023 (0.676) r = 0.002 (0.972) r = 0.056 (0.311) r = 0.106 (0.053) r = 0.110 (0.046) r = 0.016 (0.769)

r = 0.185 (0.001) r = 0.233 (0.000) r = 0.276 (0.000) r = 0.159 (0.004) r = 0.021 (0.699) r = 0.082 (0.137) r = 0.103 (0.063) r = 0.041 (0.464) r = 0.047 (0.391) r = 0.004 (0.948) r = 0.131 (0.017) r = 0.116 (0.034) r = 0.008 (0.879) r = 0.030 (0.589) r = 0.013 (0.812) r = 0.035 (0.529) r = 0.083 (0.131) r = 0.019 (0.732) r = 0.018 (0.745)

r = 0.001 (0.991) r = 0.016 (0.776) r = 0.013 (0.816) r = 0.100 (0.072) r = 0.152 (0.006) r = 0.048 (0.388) r = 0.079 (0.161) r = 0.046 (0.410) r = 0.040 (0.474) r = 0.068 (0.224) r = 0.025 (0.660) r = 0.061 (0.275) r = 0.132 (0.017) r = 0.006 (0.911) r = 0.057 (0.308) r = 0.070 (0.207) r = 0.036 (0.518) r = 0.036 (0.520) r = 0.060 (0.278)

r = 0.077 (0.169) r = 0.104 (0.061) r = 0.107 (0.054) r = 0.103 (0.063) r = 0.016 (0.769) r = 0.043 (0.443) r = 0.059 (0.294) r = 0.027 (0.638) r = 0.069 (0.215) r = 0.012 (0.829) r = 0.048 (0.393) r = 0.042 (0.453) r = 0.021 (0.711) r = 0.007 (0.906) r = 0.094 (0.091) r = 0.013 (0.815) r = 0.002 (0.970) r = 0.003 (0.955) r = 0.007 (0.905)

GPS Dictionary Antivirus Voice call SMS MMS Game Music/ring tone Video/movie TV Ticketing/ reservation Payment Banking APP Email Online chatting Community website

for mobile services independent of that for handset or the subscription of mobile network. As stated earlier, most literature is found to be focused on a single topic of the three dimensions of mobile/smart phone demand without considering the dependent relationship among them and this study may give an empirical support to its rationality. Even so, the effect of smart phone handset attributes and mobile network subscription on mobile services such as news/ weather forecast, map/traffic and GPS is still noticeable. This is reasonable because most of these value-added services depend largely on mobile Internet access to get up-to-date information, on better CPU and larger memory to process the information, and better camera and larger screen to have better visual effect. Another results worth noting are the effect of demographic variables. From the literature review, it has been noted that some authors have pointed out the significant effect of users’ demographic characteristics on the mobile phone demand; e.g., the significant difference between male and female mobile phone users, and between young users, especially students, and the elderly. In this study, gender’s effect is observed, but is not as obvious as what have been pointed out in the literature. The most noticeable relationship is the effect of demographic variables on the attributes of mobile network. Users’ gender, age, occupation and income were found to have effect on the contract with voice and 3G Internet, and the monthly 3G Internet fee. However, the effects of demographic variables on smart phone demand for handset and value-added services are not extensive and explicit. Mobile services, especially those software referred to as mobile APPs, are believed to be a future trend which will be widely accepted and used by general public. A large of number of so-called ‘service innovation’ is actually based on these smart phones and their connection to the Internet; e.g., downloadable applications, full motion video, QR code, digital TV (Weber et al., 2011). Since mobile services are found fairly independent of how users use smart phone handset, and how they subscribe to the mobile network in this study, the mobile service innovation is expected to move forward without much technical barrier, especially in terms of the handset hardware and software and their Internet access. References Akematsu, Y., Shinohara, S., Tsuji, M., 2012. Empirical analysis of factors promoting the Japanese 3G mobile phone. Telecommun. Pol. 36, 175–186.

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