The pervasiveness of Mobile Data Services: Do usage and attitudinal divides exist in Asia and North America?

Int. J. Electronic Business, Vol. 9, Nos. 1/2, 2011

The pervasiveness of Mobile Data Services: Do usage and attitudinal divides exist in Asia and North America? Lena Stephanie*, Margaret Tan, Miguel Morales-Arroyo and Ravi S. Sharma Special Interest Group on Interactive Digital Enterprises – SIGIDE & Wee Kim Wee School of Communication & Information, Nanyang Technological University, 31 Nanyang Link 637718, Singapore E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] *Corresponding author Abstract: Mobile Data Services (MDSs) are a wide array of applications provided via the mobile network and platform, where industry players are keenly exploring innovations to launch the next big thing, especially with the rapid increase and saturation of mobile voice subscriptions. This study investigates mobile user behavioural intentions towards MDS based on an adaptation of the Unified Theory of Acceptance and Use of Technology (UTAUT). Data was collected in a survey of over 1600 mobile users in Singapore and Los Angeles (LA). The study found that LA-based mobile users have a far more positive disposition than Singaporean users towards MDS adoption, and that differences on account of prior experience were also more distinct amongst LA users than their Singapore counterparts. It is hoped that these findings will serve as design rules for mobile service providers in more sophisticated markets. Keywords: MDSs; mobile data services; broadband data services and applications; mobile users. Reference to this paper should be made as follows: Stephanie, L., Tan, M., Morales-Arroyo, M. and Sharma, R.S. (2011) ‘The pervasiveness of Mobile Data Services: Do usage and attitudinal divides exist in Asia and North America?’, Int. J. Electronic Business, Vol. 9, Nos. 1/2, pp.63–105. Biographical notes: Lena Stephanie has a Bachelor of Engineering (1993) from PSG College of Technology, India, and a Master of Business Administration (1995) from Anna University, India. She has over 10 years’ industry experience spanning the areas of marketing research, consultancy and information technology. She started her career with Gallup, India, and was working with Ngee Ann Polytechnic, Singapore, prior to beginning her PhD candidacy at Nanyang Technological University, Singapore. As a certified Project Management Professional (PMP), she has successfully managed several researches and IT projects in her career. Currently, she is a PhD student with her research interest in the area of e-health business models.

Copyright © 2011 Inderscience Enterprises Ltd.

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L. Stephanie et al. Margaret Tan is an Associate Professor at the School of Communication and Information and Deputy Director at the Singapore Internet Research Centre at Nanyang Technological University in Singapore. She has published widely in scholarly journals and authored the following books: “The Virtual Workplace” and “e-Payment: The Digital Exchange”. She has been invited to speak at various international conferences and seminars as well as served as programme chairs of international conferences. She has also served numerous editorial and review boards of international journals and publications. Miguel Morales-Arroyo is a Researcher in the Applied Mathematics and Systems Institute in the National University of Mexico. He was a research fellow and a lecturer in Nanyang Technological University in Singapore. He held the position of Assistant Professor in knowledge management programme at the University of Oklahoma. He has worked in the public and private sectors. As a freelance consultant for a consulting company in Mexico, he worked on transportation problems, project feasibility and system solutions. He studied his Interdisciplinary PhD in the University of North Texas (Fulbright Recipient), his Master’s Degree in Systems Engineering and undergraduate studies in Engineering in the National University in Mexico. His research interests are related to information management studies, where information, knowledge and technology are essential to the decision-making process. Ravi S. Sharma is an Associate Professor at the Wee Kim Wee School of Communication and Information at the Nanyang Technological University since January 2004. He had spent the previous 10 years in industry as Asean Communications Industry Principal at IBM Global Services and Director of the Multimedia Competency Centre of Deutsche Telekom Asia. His teaching, consulting and research interests are in knowledge and digital economic strategies. He has (co-)authored over 100 technical papers and his work has appeared in leading journals, conferences, trade publications and the broadcast media.

1

Introduction

1.1 Mobile Data Services Wireless Mobile Data Services (WMDSs) have been defined as encompassing all types of digital data services accessible through any type of mobile devices via wireless networks (Lu et al., 2008). MDSs may, in a way, be considered a technological revolution inasmuch as they provide users, regardless of their location, with a constant source of information in addition to offering them highly personalised services (Hong et al., 2006). The rapid diffusion of internet-enabled mobile phones has accelerated the development of MDS, which, in turn, has facilitated the development of mobile commerce (m-commerce). Enabling consumers to meet business and leisure needs anywhere anytime, the ubiquitous characteristics of mobile applications indeed constitute a tremendous m-commerce potential, which could ultimately turn out to be the driving force for the next wave of electronic commerce (Liang and Wei, 2004).

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1.2 Growth of the mobile marketplace With average global mobile penetrations estimated to register a 75% increase by 2011 (Paul Budde Communication Pty Ltd., 2009), mobile network operators are turning to data services that can generate new revenue streams to increase the Average Revenue Per User (ARPU). With the commoditisation of voice and long-distance, saturated and developed markets, the telecommunication industry is forced to find new revenue streams, as even new players such as media companies, content providers and application developers are all becoming more and more involved in this market segment (Telecomsmarketresearch.com and BuddeComm, 2009). Asia’s contribution to global mobile penetration is estimated to grow more than 50% by 2013 (Ho, 2008), thereby making it one of the fastest-growing markets in the world. Indeed, developing telecommunications markets in Asia are busy building infrastructure, restructuring marketplaces and, most significantly, facilitating and contributing to healthy subscriber growth. The developed economies, in turn, are aggressively moving into more advanced networks and value-added services, while endeavouring to establish themselves, at the same time, among the world leaders in telecom services. For instance, Japan, South Korea, Australia, Singapore and Taiwan are said to be well placed to exploit the mobile internet and lead in the diffusion of MDS as a means to retain high-value subscribers as well as increase ARPU. It is thus evident that the diffusion process of MDS will vary across different geographic markets with parts of Asia taking the lead, possibly ahead of Europe and the USA in terms of both mobile and 3G penetration. As for Singapore’s mobile sector, which is considered mature given its mobile penetration at 137% (IDA Singapore, 2010), the country nevertheless continues to witness growth in terms of 3G penetration and uptake of MDS (BuddeComm, 2009a). As of early 2009, 3G subscriptions represented an imposing 40% of the total mobile subscriber base (BuddeComm, 2009a) and MDS usage is expected to grow, fostered by the rollout of higher mobile broadband speeds and affordable smartphones. According to analysts at Frost and Sullivan, services like mobile TV, mobile payments and location-based services are more likely to progress in the future (ZDNet Asia, 2009).

1.3 The objectives of the study This study investigates mobile user behavioural intentions towards MDS based on an adaptation of the UTAUT. It compares two geographically diverse MDS markets – the city-state of Singapore and LA in the USA. Both regions have a strong history of early technology adoption, but have great diversity in terms of market structure and user profiles. LA is the second largest city in the USA and shares with Singapore the status of ‘alpha-world city’. As global cities, both Singapore and LA are characterised by advanced communication infrastructures such as Wi-Fi networks and cellular phone services, although Singapore leads the USA as a whole in terms of Networked Readiness Index (NRI), according to the World Economic Forum’s Global Information Technology Report 2009–2010; Singapore ranks second in NRI, well ahead of the USA at fifth place (World Economic Forum, 2010). Both countries are home to heterogeneous populations, share relatively free markets and are at the forefront of technology. However, one obvious differentiator is the size of the countries – while the latter is the third largest country in the world, the former is a city-state and one of the smallest. In terms of mobile penetration, it is curious that Singapore has one of the highest mobile

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penetration rates in the whole of Asia at about 137% (IDA Singapore, 2010) whereas the USA has one of the lowest mobile penetration rates among developed countries (Business Monitor International, 2009a) at 90% (BuddeComm, 2009b). In terms of 3G penetration, Singapore led at about 30% compared with 16% in the USA in 2007 (Business Monitor International, 2009b). Several studies have explored the role of geography, gender and prior experience in MDS adoption and use. This study addresses geography from a cultural perspective because of the strong influence cultural characteristics have been found to exert on MDS behaviour. For example, Choi et al. (2006), based on a qualitative study of Korean, Japanese and Finnish MDS users, found that critical user-experience attributes were correlated with characteristics of the user’s culture whilst Bina and Giaglis (2007) studied Greek and Korean MDS adopters to understand the differences and similarities in how they perceived the value of MDS within personal and business life domains. Li et al. (2008) investigated gender differences in the adoption and use of m-commerce of respondents enrolled in a business college in the USA, whilst Jiang (2008) found gender to be a significant influencer of mobile internet adoption. Kim et al. (2009), based on a study of Korean students, found that the antecedents leading to MDS user behaviour varied depending on prior experience. In this context, this study addresses the following research questions: •

Does geographic location influence mobile users’ behavioural intentions towards MDS?

•

Does gender influence mobile users’ behavioural intentions towards MDS in the two geographic markets? Do such influences vary across the two markets?

•

Does prior experience influence mobile users’ behavioural intentions towards MDS in the two geographic markets? Do such influences vary across the two markets?

The remainder of this paper is structured as follows: Background literature is reviewed in Section 2, followed by a description of the research framework adopted in Section 3. Section 4 is an account of the field research methodology adopted in the study. Following this section is an analysis of the survey results and a discussion of whether the results support the research questions. The paper concludes with an unbiased discussion of some of the major limitations of the study and suggestions for further research.

2

Literature review

This section reviews relevant literature, first defining MDS in this study’s context, then detailing consumer behaviour theories, and finally providing an introduction to IS theories.

2.1 MDS as defined in the study While there are various definitions of MDS in scholarly and trade literatures, two easy-to-comprehend and relevant ones are – “wireless access to an assortment of data services using a mobile phone” (Bina and Giaglis, 2007, p.1) and “an assortment of digital data services that can be accessed using a mobile device over a wide area” (Hong and Tam, 2006, p.164). Although the term MDS connotes a variety of

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sophisticated information and data services including access to digital content on the internet via wireless devices, SMS, the basic data service, remains the most popular segment of them all. With an estimated 2 trillion messages sent worldwide in 2008 and mobile messaging revenues estimated to account for US$ 65–75 billion in 2008 (Telecomsmarketresearch.com and BuddeComm, 2009), SMS makes a significant component of MDS and cannot be overlooked. In this context, this study acknowledges SMS as an MDS, and, accordingly, MDS is defined in this study as “digital data services that you access through your mobile phone (e.g., e-mail, MMS, SMS, news/weather information, audio or video clip downloads, internet browsing) excluding voice calls” (Sharma and Felix, 2008, p.3). MDS may also include messaging services, mobile e-mail, premium-priced text (e.g., receiving an MMS on weather forecast), downloads to devices (e.g., games, ringtones, music, etc.), access to news through a mobile phone, mobile ticket reservations, mobile stock trading, and so on.

2.2 MDS consumer behaviour and expectations Comparing the extensive work done on consumer behaviour towards technology and innovation, research on mobile consumer behaviour is still in its infancy. With the global MDS market estimated to reach US$ 80 billion by 2011 and the number of MDS users worldwide projected to reach about 1 billion around the same time (IBM, 2008), equipment manufacturers, mobile network operators and internet service providers are all busy innovating new products and services to satisfy consumer needs. A necessary offshoot of this dynamic nature of the MDS diffusion process is the challenges it poses for the industry to be in tune with emerging consumer needs as well as consumer attitudes and behaviours. According to an IBM global survey conducted in early 2008 (IBM, 2008), consumers generally want more control and choices on the mobile internet and prefer a service provider that gives them a wider choice of applications and services on their mobile devices. The study found that consumers worldwide increasingly favour MDS offering utilitarian or entertainment value. In mature markets, MDS is predicted to extend and complement the personal computer, while in emerging markets, consumers are often found to skip their first personal computer purchase and head straight for high-end mobile platforms that offer the similar services.

2.3 MDS and IS theories Although there are a multitude of theories that address individual acceptance and usage of technologies, such as the Technology Acceptance Model (Davis et al., 1989), the Theory of Planned Behaviour (Ajzen, 1991) and the Diffusion of Innovation Theory (Rogers, 1995), these theories, however, are not very comprehensive as they do not adequately account for social influence or explain user intentions (López-Nicolás et al., 2008). Indeed, there has been increasing concern about the appropriateness and comprehensiveness of TAM and similar theories as they are not only parsimonious (i.e., not very relevant for contexts other than organisational) but also deterministic, and tautological (Bouwman et al., 2005). Although these theories have been widely used to explain IS adoption in workplace, Kim and Han (2009) suggest that they may not sufficiently explain individual user acceptance of MDS, a pay-per-use service.

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Research model and hypotheses

3.1 Research model The focus of this study is to understand differences in mobile user behavioural intentions towards MDS in the two geographic markets of Singapore and USA by examining the main effects of the three moderators, viz. geographic location, gender and prior experience on determinants of behavioural intentions towards MDS. Among the determinants investigated include social influence and facilitating conditions, with variables designed to determine the extent of their influence on mobile user behavioural intentions. The UTAUT model (Venkatesh et al., 2003), a synthesis of eight different theories of innovations namely Theory of Reasoned Action, Technology Acceptance Model, Motivational Model, Theory of Planned Behaviour, Combined Theory of Planned Behaviour/Technology Acceptance Model, Model of Personal Computer Utilisation, Diffusion of Innovation Theory and Social Cognitive Theory, is considered an appropriate framework for this study. The model, shown in Figure 1, holds that the four key constructs of performance expectancy, effort expectancy, social influence and facilitating conditions are direct determinants of usage intention and behaviour while gender, age, experience and voluntariness of use are posited to mediate the impact of the four key constructs on usage intention and behaviour. Figure 1

UTAUT research model

Source: Adopted from Venkatesh et al. (2003, p.447)

This study, however, adapts UTAUT by incorporating two additional constructs, namely perceived value, which includes utilitarian, hedonic and social values, and monetary value. This is because perceived value and monetary value have been found to be significant determinants of acceptance and usage of pay-per-use services like MDS. In both IS and marketing disciplines, utilitarian, hedonic and social values cover a broad set of values, which are perceived to be important in pay-per-use services (Kim and Han, 2009).

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In the pay-per-use context, factors that relate to the cost of system usage are also considered crucial (Turel et al., 2007). This adaptation model illustrated in Figure 2 merges the technology-related constructs of performance expectancy (perceived usefulness) and effort expectancy (perceived ease of use) to form the construct of technology. Appendix 1 lists the constructs with the corresponding variables in the survey instrument. It is critical to understand the mechanism leading to MDS user behaviours to foster MDS diffusion (Sohn and Kim, 2008), as there is compelling evidence in scholarly and trade literatures that points to a lack of universalism in the adoption and use of technology, in general, and of MDS, in particular. The hypotheses for this study were hence formulated in an attempt to understand the moderating roles of geographic location, gender and prior experience in shaping mobile users’ behavioural intentions. Figure 2

Adaptation of UTAUT as research model

Source: Adapted from Venkatesh et al. (2003)

3.2 Hypotheses development 3.2.1 Geographic location Many IS researchers who studied cultural issues have often equated country with culture (e.g., Honold, 1999; Smith et al., 2004). Studies by Ford et al. (2003) and Zakaria et al. (2003) have found that cultural factors exert an influence above and beyond the influence of economic and environmental factors on how ICT products and services are used in a country. For MDS, cross-national and cross-regional differences in users’ design preferences may be more substantial than they are for the internet. This is because the internet offers universal access, whereas the wireless networks may only operate within specific regions (Chae and Kim, 2003).

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It is generally held that MDS is more widely used in Asia than in the rest of the world including the USA. Yap (TNS Technology, 2009)1 suggests reasons underpinning the significantly higher adoption of advanced mobile technologies in Asia. Technology tends to be more aspirational for Asian consumers as they tend to be early adopters. A corollary of this tendency is the early and significant investments in mobile infrastructure by industry players, and, in some cases, by governments, which have led to network capabilities that today surpass those of Europe and North America. Overall, investments in telecommunication infrastructure, improving network speeds, a focus on innovation and affordable flat-rate data plans have made the developed Asian region the world’s most advanced in mobile technology adoption (TNS Technology, 2009). It is, therefore, hypothesised that geographic location has significant effects on the determinants of mobile users’ behavioural intentions towards MDS: Hypothesis 1: Mobile users in Singapore and Los Angeles differ in their behavioural intentions towards MDS usage.

3.2.2 Gender Several empirical studies focus on gender divide in technology adoption. Allyn (2003) found that men and women used the computer for different purposes at work. Wood and Li (2005) suggested that males were more willing to adopt new technologies than females. There are studies that suggest that gender gaps are narrowing due to increased exposure of computers in the work as well as personal life (Rainer et al., 2003). There are even studies that challenge interpretations of observed gender differences based on biological sex rather than on self-concept traits associated with gender identity (Hupfer and Detlor, 2009). As for MDS adoption, it has been found, for instance, that men are more likely to use their phone for commerce much more than women (Nielsen Mobile, 2008) and that different types of MDS have a different appeal for men and women (IT Facts, 2008). Consequently, it is hypothesised that gender has significant effects on the determinants of mobile users’ behavioural intentions towards MDS in the two geographic markets, and that these main effects vary across these two markets: Hypothesis 2: Mobile users in Singapore and Los Angeles differ in their behavioural intentions towards MDS usage with gender being a determining factor.

3.2.3 Prior experience Several IS studies have noted the role of prior experience in determining behaviour. For instance, Ajzen and Fishbein (1980) proposed that prior experience enhances the salience of low-probability events and ensures they play a role in the formation of intentions. Taylor and Todd (1995) found that inexperienced users’ emphasis on the determinants of intention and usage is different from that of experienced users. Kim et al. (2009) suggested that, for inexperienced MDS users, perceived usefulness has a more positive effect on adoption intentions whereas for experienced MDS users, perceived enjoyment plays a greater role in generating positive user behaviour. It is, therefore, assumed that prior experience has significant main effects on the behavioural

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intentions of mobile subscribers towards MDS and that these effects may vary across the two geographic markets. Hypothesis 3: Mobile users in Singapore and Los Angeles differ in their behavioural intentions towards MDS usage with prior experience being a determining factor.

4

Research method

4.1 Worldwide Mobile Data Services Survey (now Global Mobile Survey – GMS) The data for this study was collected as part of the Worldwide Mobile Data Services Survey (WMDSS). The current study compares a slice of the data collected in Singapore with that from LA County, USA. The WMDSS is an annual survey study involving a consortium of universities and research institutions located around the world. Initiated at the first Global Mobility Roundtable held in Tokyo in 2002, the WMDSS project began with three member countries, namely Korea, Japan and Hong Kong, which were incidentally among the leading markets for MDS. Consortium participants for the 2008 survey included Australia, Canada, China, Denmark, Finland, Greece, Hong Kong, Hungary, Japan, Korea, Singapore, Spain, Sweden, Taiwan and the USA. The objective of the survey is to gain a better understanding of mobile service consumer behaviour in international markets.

4.2 Research instrument and data collection The research instrument used by the member countries was a jointly developed survey questionnaire comprising three broad sections: demographics, use of data services via the mobile phone and views on mobile phone services. It was validated by the WMDSS consortium experts and tested for reliability through pilot tests conducted in every participating country. Discussion of the design of the research instrument and its validity and reliability is beyond the scope of this paper. Full details of the Singapore survey are available at SSRN (Sharma and Felix, 2008). In Singapore, the responses were solicited through a combination of field and online surveys. A 5-point Likert scale was used for the questionnaire. Field surveys were conducted in three high-traffic customer stores of a leading mobile service provider whereas online surveys were circulated via e-mail to a random sample of potential participants obtained from numerous sources. In LA County, the surveys were conducted in five different customer stores of a leading national mobile service provider. The total sample size obtained in LA and Singapore was 1106 and 527, respectively.

5

Data analysis and results

A series of five sets of independent samples T-tests incorporating Levene’s test for equal variances were performed. For the first set of tests, the sample was grouped by the factor geographic location. The next four sets of tests were performed individually on the

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Singapore and LA samples, each grouped by the factors gender and prior experience. Each set of tests was carried out on the 50 variables/items listed in Appendix 1. Only those variables presenting evidence of significant differences in respect of any of the grouping (moderating) factors were shortlisted for the next level of analysis. At the next level, a two-way ANOVA in a model comparison approach (full vs. reduced models) using design matrices (Maxwell and Delaney, 2004) was used to test for main effects of the three moderating factors on the shortlisted variables that involved another series of three sets of main effects tests. The formulation of the full and reduced models, and the results of the main effect tests are presented in Appendix 2. The main effect tests were interpreted only when the moderating factor in question was involved in no significant two-way interaction with the other two factors. In all tests, confidence levels were set at 95% (i.e., p = 0.05).

5.1 Main effects of geographic location Table 1 shows the demographic and usage data of the respondents. The Singapore respondents spent relatively more time online at home but used MDS less widely than their LA counterparts. Although the most significant use of MDS in both groups was communication, Singapore MDS users had comparatively heavier usage of communication, whereas LA MDS users tended to out-do their Singapore counterparts in the areas of purchasing, information and entertainment. Curiously, Singaporeans seemed more inclined to use MDS for personal reasons; however, they showed a lower level of interest in MDS of entertainment value. They were also less willing to pay a higher monthly subscription fee for MDS. Hypothesis 1 – Mobile users in Singapore and LA differ in their behavioural intentions towards MDS usage – this hypothesis was partially supported. Table 2 shows the results of the main effects tests on the moderating factor of geographical location (significant main effects are highlighted). It is noted that the LA respondents were significantly more likely to subscribe to ten (10) or more of the eighteen (18) MDS offering utilitarian, hedonic and social values. The only area where both the LA and the Singapore respondents were in accord was a set of six (6) MDS of utilitarian value. It seemed that Singapore respondents were not as inclined towards MDS of hedonic and social values as their LA counterparts. While both the LA and the Singapore respondents were equally well motivated to subscribe to MDS that they found useful, Singapore respondents were more likely to be put off by MDSs that were difficult to use or took too long to learn. Also, they were more sensitive to the price and value of MDS when compared with LA users. Higher prices and perceptions of inadequate value may dissuade them from subscribing to MDS as the study found that Singapore MDS users were willing to spend about $11 a month vis-a-vis their LA counterparts who were willing to pay more than twice as much ($27). Social influence seemed to play a greater role in impacting the behavioural intentions of LA respondents; they were more likely to use an MDS if they saw their friends, colleagues or other people using it. Also, they would be motivated to use MDS if there were better network quality and handsets. As for network coverage, the main effect of geographic location was ambiguous and thus not interpreted. Such ambiguity may be a result of the factor’s significant interaction with the other moderating factors. As for the next handset purchase, the LA respondents attached greater importance to features like

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fast access to MDS, music/video downloads, qwerty keyboard, larger screen size and touch screens. Table 1

Gender:

Profile of respondents – Singapore and Los Angeles Singapore

Los Angeles

N = 527

N = 1106

Female

47%

47%

Male

53%

53% (N = 527)

Average time spent online at home/day

88 minutes

Average monthly fee willing to pay for MDS

USD 11

MDS adoption

65%

(N = 1068) 71 minutes

(N = 524)

(N = 1042) USD 27

(N = 525)

(N = 1056) 74%

(N = 527) MDS users only

N = 341

(N = 1106) N = 821

Frequency of use of MDS for:

Mean

Communicating

4.27

Information

2.35

Entertainment

2.05

Purchasing

1.68

4.08 (SD = 1.18, N = 816)

(SD = 1.08, N = 339) 3.29

(SD = 1.36, N = 815)

(SD = 1.22, N = 340) 3.05

(SD = 1.39, N = 815)

(SD = 1.14, N = 340) 2.48

(SD = 1.35, N = 805)

(SD = 0.97, N = 336) Three most important reasons to use MDS

Mean Useful: 4.07

Useful: 4.07

(SD = 1.10, N = 774)

(SD = 1.11, N = 338)

Informative: 3.94

Easy to use: 3.97

(SD = 1.16, N = 754)

(SD = 1.16, N = 338)

Easy to use: 3.93

Informative: 3.82

(SD = 1.15, N = 769)

(SD = 1.19, N = 337) MDS use mainly/exclusively for personal activities

49%

Average MDS usage/week

39 minutes

Average No. of text messages sent/week

91

41% (N = 339)

(N = 784) 58 minutes

(N = 340)

(N = 794) 91

(N = 339)

Individual country sample sizes may not add up to the total sample size due to missing data.

(N = 798)

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Table 2

Singapore vs. Los Angeles respondents – statistical results Singapore

Construct item

Los Angeles

N

Mean

S.D.

Scheduling

521

2.16

Mobile internet search

526

2.71

News

522

Mobile banking services Ability to pay for things through your phone

I

N

Mean

S.D.

1.16 ** 1050

2.62

1.38

1.30 ** 1048

3.16

1.35

2.66

1.26 ** 1040

2.84

1.37

524

2.68

1.31 ns 1034

2.68

1.43

523

2.77

1.36 ns 1043

2.83

1.46

PERCEIVED VALUE

1. Utilitarian

Location-based services

524

2.57

1.29 ** 1044

2.82

1.39

Stock tracker/alert

525

2.23

1.29 ** 1042

2.41

1.43

Traffic alerts

523

2.51

1.28 ** 1046

3.01

1.44

Maps, GPS

526

2.80

1.32 ** 1051

3.33

1.40

GPS tracking services for children

519

2.31

1.33 ** 1036

2.78

1.51

Services targeting ethnic background

524

2.25

1.22 ** 1034

2.47

1.44

Services to monitor chronic health diseases

523

2.31

1.21 ns 1048

2.41

1.40

Games to help children monitor their chronic diseases

521

2.02

1.15 ** 1051

2.31

1.42

Remote monitoring services for the elderly

522

2.30

1.26 ns 1052

2.39

1.45

Services to support learning and education

523

2.47

1.28 ns 1055

2.60

1.46

Games

520

2.21

1.18 ** 1025

2.75

1.45

Entertainment

518

2.47

1.26 ** 1034

2.95

1.44

522

2.20

1.19 ** 1035

2.76

1.48

525

3.47

1.28 ** 1013

3.62

1.29

2. Hedonic

3. Social Social networking sites II

TECHNOLOGY

1. Usefulness Availability of more useful services 2. Ease of use Ease of use

522

3.56

1.30 ns 1017

3.67

1.30

Difficult to use

522

3.27

1.41 ** 1034

2.85

1.45

Complicated

517

3.27

1.40 ** 1040

2.87

1.47

Learning takes too long

519

3.05

1.44 ** 1039

2.72

1.46

III MONETARY VALUE Promotional deals

523

2.99

1.30 ns 1025

3.12

1.45

Fall in prices

526

3.42

1.35 ** 1037

3.59

1.37

Expensive

522

3.81

1.34 ** 1061

3.54

1.40

Don’t see the value IV SOCIAL INFLUENCE Friends and colleagues use services Seeing other people using services

510

3.31

1.39 ** 1035

2.80

1.50

523 522

2.91 2.47

1.28 ** 1030 1.24 ** 1031

3.28 2.99

1.39 1.41

The pervasiveness of Mobile Data Services Table 2

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Singapore vs. Los Angeles respondents – statistical results (continued) Singapore

Construct item V FACILITATING CONDITIONS 1. Infrastructure Network quality Network coverage 2. Device Better handsets for easier access to wireless services More modern design New communication features More data-oriented features Better functionality for games Multi-functions Better overall performance Fast access to Mobile Data Services Simpler to use Music downloads Video downloads Qwerty keyboard Larger screen Touch screen Operating system Battery life Size and weight Design Memory/storage capacity

Los Angeles

N

Mean

S.D.

526 525

3.40 3.41

525 522 524 523 524 523 524 517 523 521 524 519 524 521 522 521 523 521 518

N

Mean

S.D.

1.30 ** 1034 1.30 ** 1028

3.69 3.72

1.31 1.30

3.29

1.37 ** 1041

3.52

1.34

3.39 3.36 3.22 2.65 3.50 4.00 3.56 3.72 2.61 2.52 2.83 3.48 3.25 3.27 4.07 4.04 3.82 4.07

1.29 1.27 1.25 1.32 1.32 1.15 1.31 1.26 1.33 1.30 1.37 1.32 1.40 1.39 1.18 1.18 1.26 1.16

3.41 3.57 3.42 2.85 3.60 4.03 3.81 3.75 3.12 3.03 3.39 3.75 3.57 3.61 4.09 3.97 3.85 3.94

1.36 1.32 1.34 1.49 1.38 1.18 1.28 1.25 1.47 1.48 1.43 1.28 1.37 1.38 1.12 1.17 1.23 1.19

ns ** ** ** ns ns ** ns ** ** ** ** ** ** ns ns ns ns

1051 1050 1041 1041 1043 1056 1042 1054 1041 1042 1037 1048 1033 1014 1055 1043 1049 1045

**Significant. ns: Not significant (Independent samples T-test). Significant main effect of geographic location (2-way ANOVA model comparison).

5.2 Main effects of gender The descriptive statistics of gender differences are presented in Table 3. Gender differences were more discernible in Singapore than in LA. For instance, males in Singapore spent more time online at home and their MDS adoption rate was higher than that of their female counterparts. Although MDS was predominantly used for communication, the usage for information was higher for males. Females, on the other hand, tended to use MDS more for personal reasons than for work reasons; they also sent more text messages and used MDS for longer durations. In the LA group, except for the finding that female users were significantly heavier users for communication services, the MDS usage patterns differed only marginally between the male and female respondents.

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Table 3

Profile of respondents – gender Singapore N = 527 Female N = 246

Average time spent online at home/day Average monthly fee willing to pay for MDS MDS adoption MDS users only Frequency of use of MDS for: Communicating

84 minutes (N = 245) USD 10 (N = 245) 62% (N = 246) N = 152

Los Angeles N = 1106

Male N = 281

Female N = 500

91 minutes (N = 279) USD 12 (N = 280) 67% (N = 281)

70 minutes (N = 482) USD 27 (N = 483) 73% (N = 500)

N = 189

Male N = 568 72 minutes (N = 541) USD 28 (N = 550) 76% (N = 568)

N = 381

N = 412

Mean 4.37 (SD = 1.04, N = 151)

4.19 (SD = 1.10, N = 188)

4.21 (SD = 1.13, N = 377)

4.02 (SD = 1.17, N = 409)

Information

2.15 (SD = 1.19, N = 151)

2.51 (SD = 1.23, N = 189)

3.21 (SD = 1.37, N = 379)

3.40 (SD = 1.34, N = 408)

Entertainment

2.10 (SD = 1.19, N = 151)

2.02 (SD = 1.10, N = 189)

3.12 (SD = 1.38, N = 377)

3.04 (SD = 1.40, N = 409)

Purchasing

1.55 (SD = 0.82, N = 150)

1.78 (SD = 1.07, N = 186)

2.48 (SD = 1.40, N = 374)

2.50 (SD = 1.31, N = 402)

Useful: 4.14 (SD = 1.06, N = 150) Easy to use: 4.03 (SD = 1.17, N = 150) Informative: 3.82 (SD = 1.13, N = 149)

Useful: 4.02 (SD = 1.16, N = 188) Easy to use: 3.93 (SD = 1.15, N = 188) Informative: 3.81 (SD = 1.25, N = 188)

Mean Useful: 4.07 (SD = 1.04, N = 368) Easy to use: 3.96 (SD = 1.11, N = 362) Informative: 3.92 (SD = 1.14, N = 354)

Useful: 4.11 (SD = 1.12, N = 389) Informative: 3.97 (SD = 1.17, N = 384) Easy to use: 3.93 (SD = 1.16, N = 390)

52% (N = 151) 42 minutes (N = 152) 95 (N = 151)

42% (N = 188) 35 minutes (N = 188) 87 (N = 188)

39% (N = 365) 56 minutes (N = 368) 92 (N = 372)

36% (N = 396) 60 minutes (N = 402) 87 (N = 402)

Three most important reasons to use MDS

MDS use mainly/exclusively for personal activities Average MDS usage/week Average No. of text messages sent/week

Individual cell sample sizes may not add up to the total sample size due to missing data.

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Hypothesis 2 – Mobile users in Singapore and LA differ in their behavioural intentions towards MDS usage with gender being a determining factor – this hypothesis was partially supported as shown in Table 4. It may be noted that the main effects of gender were few, and these effects were more conspicuous in Singapore than in LA. Table 4

Female vs. male respondents – statistical results

78 Table 4

L. Stephanie et al. Female vs. male respondents – statistical results (continued)

The pervasiveness of Mobile Data Services Table 4

Female vs. male respondents – statistical results (continued)

79

80 Table 4

L. Stephanie et al. Female vs. male respondents – statistical results (continued)

In both Singapore and LA, behavioural intentions were most positively influenced by MDS of utilitarian value for all. The services where there were gender differences

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were news and payment through mobile phones; Singapore men showed a stronger preference for these services than women. This corresponds with the finding that MDS use for information is much higher for the Singapore men than for the women. As for the LA group, no gender effects were observed on behavioural intentions towards any of the 18 MDS of utilitarian, hedonic and social values. Both genders in Singapore and LA were similarly motivated to subscribe to MDS that they found useful. While no gender effects on technology perceptions existed in the Singapore group, some effects were observed in LA. The LA females differed from their male counterparts in that they were more likely to be put off by MDSs that were difficult or complicated to use. No significant main effects of gender on the respondents’ monetary value perceptions were observed in either the Singapore or the LA samples. Men and women alike in either geographic location were found to be equally reluctant to subscribe to MDS that were expensive or lacked value. They were also equally willing to subscribe to MDS if lured with promotional deals or a fall in prices. Also, gender did not have any main effects on how social influence impacted the behavioural intentions of men and women in Singapore and LA. Both men and women in LA were more or less equally willing to try MDS if better network quality and network coverage were perceived to be offered by their service provider. In Singapore, however, men, rather than women, tended to attach more importance to network coverage. With respect to buying a new handset, gender effects were observed both in Singapore and in LA. However, these effects were found to be stronger in Singapore than in LA. Singapore males attached greater importance to data-oriented features, multi-functions, fast access to MDS, touch screen, operating system and overall performance than their female counterparts. LA males differed from their female counterparts in their stronger preference for functionality for games, multi-functions and touch screen.

5.3 Main effects of prior experience Table 5 shows the profiles of experienced and inexperienced MDS users both in Singapore and in LA. Not surprising to note is that experienced MDS users were found to spend more time online than their inexperienced counterparts, and quite predictably, they demonstrated a willingness to pay a higher monthly fee for MDS. Table 5

Profile of MDS users – experienced vs. inexperienced Singapore

LA

N = 527

N = 1106

Experienced

Inexperienced

Experienced

Inexperienced

N = 341

N = 186

N = 821

N = 285

Average time spent online at home/day

93 min

77 min

75 min

59 min

Average monthly fee willing to pay for MDS

USD 12

(N = 339) (N = 339)

(N = 185) USD 9 (N = 186)

(N = 775) USD 29

(N = 267) USD 22

(N = 784)

Individual cell sample sizes may not add up to the total sample size due to missing data.

(N = 272)

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Hypothesis 3 – Mobile users in Singapore and LA differ in their behavioural intentions towards MDS usage with prior experience being a determining factor – this hypothesis was partially supported as shown in Table 6. It was found that the main effects of prior experience on mobile user behavioural intentions were far more pronounced in the LA sample than in Singapore. Table 6

Experienced vs. inexperienced MDS users

The pervasiveness of Mobile Data Services Table 6

Experienced vs. inexperienced MDS users (continued)

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84 Table 6

L. Stephanie et al. Experienced vs. inexperienced MDS users (continued)

The pervasiveness of Mobile Data Services Table 6

Experienced vs. inexperienced MDS users (continued)

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The top drivers of positive behavioural intention among both experienced and inexperienced users regardless of their geographic location were MDS of utilitarian value. Nevertheless, significant differences in preferences still existed between the two groups, and the pattern of these differences varied in the two geographic locations. The experienced users in LA were found to be consistently more inclined to subscribe to all MDSs of utilitarian value. In Singapore, on the other hand, differences between experienced and inexperienced users were rather marginal with the former group showing greater preference only for a set of four utilitarian MDSs, namely location-based services, maps/GPS, remote monitoring services for the elderly and services to support learning and education. As regards the question of whether prior experience impacts one’s technology perceptions, the pattern was similar across Singapore and LA. Both experienced and inexperienced users in either geographic location were equally motivated to subscribe to MDS they perceived as useful. It was also observed that experienced users in both countries showed a stronger preference for easy-to-use MDS than their inexperienced counterparts. Prior experience had similar effects on the monetary value perceptions of both Singapore and LA respondents. Though perceptions of higher price levels and lack of value would commonly discourage experienced and inexperienced users in either location from using an MDS, it seemed that promotional deals and a fall in prices were found to be significantly stronger motivators for the experienced group. In Singapore, prior experience did not have a large effect on how social influence impacted the behavioural intentions of the users. In the LA group, on the contrary, the main effect of prior experience was more apparent – experienced users were found to be more motivated to use an MDS if they saw their friends, colleagues or others using it. Not surprisingly, facilitating conditions such as better network quality, network coverage and handsets generally motivated experienced MDS users more than the inexperienced users. However, the main effects of prior experience on facilitating conditions were more extensive for the LA group than for Singapore. The experienced users in LA had far higher expectations of these conditions, though they were not very different from their inexperienced counterparts in their preference for simpler to use devices. In Singapore, experienced users were more strongly motivated by network quality and performance-oriented device features like better handsets for easier access to wireless services, new communication features, more data-oriented features, better functionality for games, multi-functions, fast access to MDS and the operating system.

6

Conclusions

It may be clear that there were several usage and attitudinal divides between Singapore and LA mobile subscribers. Although mobile (and particularly 3.5G) penetration rates are far higher in Singapore, MDS adoption and using MDS for advanced or sophisticated uses such as purchasing, information and entertainment were apparently more widespread among LA mobile users. This is quite understandable when we consider the fact that a good deal of innovative technology, in a sense, flows from the USA into Singapore. Consequently, US mobile users attach more importance to facilitating conditions like the wireless network and handsets. That the LA respondents were willing

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to pay a far higher monthly subscription fee for MDS than their Singapore counterparts underscores their technology savviness. The study also revealed that US MDS consumers are more open to trying out a variety of services regardless of whether the services offered are utilitarian, hedonic or social. It may be pertinent to note in this context that a survey by Frank N. Magid Associates2 revealed that as much as 51% of the US mobile phone users access internet-based content on a weekly basis via their mobile phones. The survey also showed that non-utilitarian content such as mobile games, music (hedonic) and social networking activities (social) were just as popular with US users as utilitarian services. However, this study revealed that more sophisticated utilitarian services like mobile banking, phone payments, stock alert, monitoring chronic diseases, remote monitoring of elderly people and support for learning have a similar appeal to both LA and Singapore respondents, though the likelihood of their subscribing to such services was just low to moderate. A study by Neilsen Mobile (2008) shows the USA leads the world in mobile internet penetration with 15.6% of mobile subscribers actively accessing the internet through their mobile network. This may be attributed to the prevalence of sophisticated applications and fixed-fee unlimited data packages available in the US market. Perhaps, these facts suggest that the Singapore MDS market is still in a nascent stage and lagging behind more sophisticated markets such as the USA. However, innovations in terms of high-end MDS are being pursued in both markets, the present context may be said to offer great opportunities to industry players; it is up to them to support development of products and services in keeping with consumer needs. A further point to note is that contrary to the pervasive belief that gender gaps are inherent in technology adoption and use, this study shows that gender differences are not very significant, particularly where MDS is concerned. A plausible explanation for this is that gender differences are far less pronounced in global cities like Singapore and LA where men and women alike are constantly exposed to sophisticated technologies both in the course of their work as well as personal life. Behavioural intentions towards MDS were, therefore, comparable for the men and women in the study, although the Singapore men gravitated more towards information-focused services vis-à-vis Singapore women, whereas the LA women’s preference was conspicuously for communication services. Furthermore, complicated or difficult-to-use MDS seemed an inhibitor for LA women rather than for LA men inasmuch as they could not access those services with ease. This is in line with findings from several other empirical studies, which suggest that women are generally less techno-enthusiastic than men (e.g., Shashaani and Khalili, 2001; Tsai et al., 2001). Needless to say, gender differences do exist, though in small measure not warranting customisation of offerings by industry players including mobile network operators, internet service providers, application developers and handset manufacturers. As hypothesised, wide gaps do exist between experienced and inexperienced MDS users in their behavioural intentions towards MDS. Experienced users have a far more positive disposition towards MDS than inexperienced users. This was particularly true for the LA group where the main effects of prior experience on user behavioural intentions were distinct. The impact of prior experience was far less pronounced in the Singapore group possibly because of the nascent stage of the Singapore MDS market. Such a finding can be explained in terms of what is known as a staged adoption pattern. This would mean that once consumers get to the stage of trying out an innovative service, they gradually get habituated to its use and at some point start seeking higher levels of sophistication in these services. This view is also consistent with the Diffusion

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of Innovation Theory (Rogers, 1995), which states that diffusion of an innovation is a five-stage decision-making process comprising awareness, interest, evaluation, trial, and finally, adoption. Understanding such differences would, therefore, enable the industry players to tap this huge pool of non-users and convert them to MDS users. Needless to say, understanding moderating factors that are important in the adoption-decision process would enable the MDS industry to employ more targeted investment and marketing techniques appropriate for each group (Kim and Han, 2009). Thus, based on the findings of this study, the following design rules are proposed for industry players: •

Geography Rule. Geography plays a major role in the culture, economic prospects and lifestyle of people, and therefore exerts a strong influence on consumer behaviour especially in respect of sophisticated services like MDS. It is important to remember that the types of MDS that gain traction in one geographic market may not catch on in another. For instance, while the USA is a leader in the mobile internet market largely on account of its fixed-fee unlimited data model (Nielsen Mobile, 2008), Japan and Korea are considered leaders in the mobile TV market mainly due to government intervention and funding (Business Monitor International, 2009b). It would make sense, therefore, to assume that to increase their viability in the market, industry players must develop a thorough understanding of the market structure and dynamics in their geographic area.

•

Prior Experience Rule. It is an established marketing axiom that up-selling of a service to current users requires less effort than acquiring new customers for the service. Industry players must, therefore, have appropriate tactics for getting customers enticed and accustomed to MDS and then progressing up the value-chain. It is also important to get to know inexperienced MDS users as well as experienced users. A sound understanding of the underlying factors inhibiting inexperienced users from MDS usage may help players appropriately target this group with a view to converting them to MDS users, as a first step.

•

Gender Rule. Although several recent studies (e.g., Nielsen Mobile, 2008; IT Facts, 2008; Li et al., 2008) have demonstrated the influence of gender on user perceptions and consumption of MDS, this study on the contrary concludes that gender does not play a significant role in MDS user behaviour. Our finding renders it redundant for industry players in sophisticated markets to customise their offerings based on gender-specific consumer attitudes towards MDS.

There are some limitations to this study that need to be acknowledged. One limitation stems from the fact that data sets from both LA and Singapore are not, strictly speaking, random samples. There was a bias arising from the fact that the LA respondents were predominantly customers of a leading mobile carrier in the USA, whereas in Singapore, the sample was overweighted to customers of a participating mobile service operator. Another limitation involves the research instrument used in the study. Although the research instrument was designed with great care to be as exhaustive as possible to gain an in-depth understanding of the mobile market consumer behaviour, there is, however, the possibility that other extraneous factors (not identified in this study) may have played some role (that was not identified and measured) in shaping respondent attitudes and perceptions. Notwithstanding these limitations, it is hoped that the results of the study discussed in this paper are valid; if not representative of the population in the two

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geographic regions, they would at least be indicative of mobile user behaviour trends in Singapore and LA. As stated in the onset, the focus of this study was limited to understanding the differences in behavioural intention caused by the main effects of factors such as geographic location, gender and prior experience. Owing to time and data constraints, only two out of the several possible demographic factors were considered in this paper – geography and gender. It would, therefore, be useful if future research included more demographic factors like age, household income, occupation and education level. Likewise, in this research, prior experience comprised only two levels – experienced and inexperienced MDS users. In future research, this factor could be more granular and include levels like non-users, light users, moderate users and heavy users. The number of constructs used to study the determinants of behavioural intention may also be expanded to make a more comprehensive study. Constructs to compare and establish links between behavioural intention and actual behaviour could also be incorporated in future research. Additionally, it would be insightful to examine the interactions among the determinants of behavioural intention. It is worth investigating, for instance, if social influence impacts behavioural intentions much more than technology perceptions. By incorporating such suggestions in future research, it may be possible to generalise and further refine the research model used in this study. These efforts would support the basis for formulating sound business design rules for industry players.

Acknowledgements The paper is part of a research programme on Business Models and Pricing Strategies in the Interactive Digital Media Marketplace funded by the National Research Foundation of Singapore. The authors of this paper are grateful to their WMDSS partners in the USA for providing the US data utilised in this study. Thanks are also due to the contributions of their international collaborators – Professors Steve Wildman, Omar El-Sawy, Francis Pereira and Marcel Machill – for their congenial intellectual interactions.

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Notes 1

Source: http://www.tnsglobal.com/news/news-1C53E642AFFB4FF79CFBF3EACA107C41.aspx Source: Industry Brief, Business Monitor International (2009c).

2

Appendix 1: Constructs and question items CONSTRUCTS

ITEM CODES – QUESTION ITEMS

Construct 1: PERCEIVED VALUE

To what extent are you, or a family member, likely to subscribe to the following services IF offered by your mobile service provider?

I

Scheduling

Utilitarian

Mobile internet search News Mobile banking services Ability to pay for things through your phone Location-based services Stock tracker/alert Traffic alerts Maps, GPS GPS tracking services for children Services targeting your ethnic background (e.g., discount calls abroad, news and entertainment) Services to monitor chronic health diseases (e.g., diabetes, heart disease) Games to help children monitor their chronic diseases Remote monitoring services for the elderly Services to support learning and education II

Hedonic

III

Social

Games Entertainment (e.g., music, video, games)

Construct 2:

Social networking sites (e.g., Myspace, Facebook)

TECHNOLOGY

To what extent would the following factors increase/decrease your level of usage of Mobile Data Services or make you use Mobile Data Services?

I

Usefulness

Availability of more useful services

II

Ease of use

Ease of use To what extent have the following issues made you less or not at all interested in using Mobile Data Services? Difficult to use Complicated Learning takes too long

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Appendix 1: Constructs and question items (continued) CONSTRUCTS

ITEM CODES – QUESTION ITEMS

Construct 3: MONETARY VALUE

To what extent would the following factors increase your level of usage of Mobile Data Services or make you use Mobile Data Services? Promotional deals Fall in prices To what extent have the following issues made you less or not at all interested in using Mobile Data Services? Expensive Don’t see the value

Usage scale used in question items Not at all

To a small extent

To a moderate extent

To some extent

To a great extent

1

2

3

4

5

Importance scale used in question items Not at all important

Somewhat important

1

2

Moderately important Important Very important 3

4

5

Appendix 2 I

Formulation of Full and Reduced Models

Full Model: Yijk = µ + α j + β k + αβ jk + ε ijk

where: Yijk:

Score of the ith subject on the shortlisted dependent variable at the jth level of factor A, the kth level of factor B

µ: αj: β k: αβjk: εijk:

Grand mean parameter Effect associated with the jth level of factor A Effect associated with the kth level of factor B Effect of the interaction of the jth level of factor A and the kth level of factor B Error for the ith subject at level j of A, level k of B

Reduced Models: Effect to be tested

Reduced model

Main effect of factor A

R1: Yijk = µ + β k + αβ jk + ε ijk

Main effect of factor B

R2: Yijk = µ + α j + αβ jk + ε ijk

Interaction effect of factor A with factor B

R3: Yijk = µ + α j + β k + ε ijk

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II

Results of two-way ANOVA (Model comparison)

1

Results of tests for main effects of geographic location

Factor A – Geographic location × Factor B – Gender-Prior MDS experience Shortlisted dependent variable Scheduling

Mobile internet search

News

Location-based services

Stock tracker/alerts

Traffic alerts

Maps, GPS

GPS tracking services for children

Services targeting your ethnic background

Model

Mean square

Fobs

Fcritical

Sig.

Full

1.711

R1

51.864

30.312

3.84

**

R2

5.195

3.036

2.60

**

0.492

2.60

ns

R3

0.842

Full

1.759

R1

42.633

24.237

3.84

**

R2

10.299

5.855

2.60

**

R3

3.362

1.912

2.60

ns

Full

1.739

R1

1.661

0.955

3.84

ns

R2

12.929

7.435

2.60

**

3.599

2.60

**

5.757

3.84

**

R3

6.259

Full

1.818

R1

10.466

R2

14.814

8.148

2.60

**

R3

0.984

0.541

2.60

ns

1.115

3.84

ns

Full

1.879

R1

2.096

R2

18.580

9.888

2.60

**

R3

3.506

1.866

2.60

ns

Full

1.903

R1

63.746

33.498

3.84

**

R2

6.105

3.208

2.60

**

0.477

2.60

ns

R3

0.908

Full

1.876

R1

79.788

42.531

3.84

**

R2

11.355

6.053

2.60

**

0.427

2.60

ns

R3

0.801

Full

2.080

R1

46.103

22.165

3.84

**

R2

11.844

5.694

2.60

**

1.109

2.60

ns

3.048

3.84

ns

R3

2.306

Full

1.848

R1

5.632

R2

10.642

5.758

2.60

**

R3

2.993

1.620

2.60

ns

The pervasiveness of Mobile Data Services 1

95

Results of tests for main effects of geographic location (continued)

Factor A – Geographic location × Factor B – Gender-Prior MDS experience Shortlisted dependent variable

Model

Mean square

Games to help children monitor their chronic diseases

Full

1.762

Games

Entertainment

Social networking sites

Availability of more useful services

Difficult to use

Complicated

Learning takes too long

Fall in prices

R1

14.58

Fobs

Fcritical

Sig.

8.275

3.84

**

R2

9.584

5.439

2.60

**

1.091

2.60

ns

R3

1.922

Full

1.838

R1

57.682

31.383

3.84

**

R2

6.443

3.506

2.60

**

1.953

2.60

ns

R3

3.590

Full

1.897

R1

40.609

21.407

3.84

**

R2

1.625

0.856

2.60

ns

4.625

2.60

**

R3

8.774

Full

1.924

R1

65.929

34.267

3.84

**

R2

5.605

2.913

2.60

**

R3

4.798

2.494

2.60

ns

Full

1.611

R1

3.986

2.474

3.84

ns

R2

19.257

11.953

2.60

**

0.490

2.60

ns

R3

0.790

Full

2.048

R1

42.905

20.950

3.84

**

R2

6.360

3.105

2.60

**

0.559

2.60

ns

R3

1.144

Full

2.083

R1

41.663

20.001

3.84

**

R2

5.658

2.716

2.60

**

0.263

2.60

**

R3

0.547

Full

2.113

R1

37.855

17.915

3.84

**

R2

4.584

2.169

2.60

ns

R3

0.353

0.167

2.60

ns

Full

1.830

R1

5.047

2.758

3.84

ns

R2

12.278

6.709

2.60

**

R3

0.972

0.531

2.60

ns

96 1

L. Stephanie et al. Results of tests for main effects of geographic location (continued)

Factor A – Geographic location × Factor B – Gender-Prior MDS experience Shortlisted dependent variable Expensive

Don’t see the value

Friends and colleagues using services

Seeing other people using services

Network quality

Network coverage

Better handsets for easier access to wireless services

New communication features

More data-oriented features

Model

Mean square

Full

1.918

R1

17.455

R2

0.590

Fobs

Fcritical

Sig.

9.101

3.84

**

0.308

2.60

ns

0.655

2.60

ns

R3

1.256

Full

2.144

R1

82.768

38.604

3.84

**

R2

3.552

1.657

2.60

ns

0.063

2.60

ns

R3

0.136

Full

1.812

R1

25.573

14.113

3.84

**

R2

12.115

6.686

2.60

**

1.144

2.60

ns

R3

2.074

Full

1.823

R1

57.989

31.810

3.84

**

R2

4.430

2.430

2.60

ns

0.812

2.60

ns

R3

1.480

Full

1.652

R1

12.565

7.606

3.84

**

R2

16.110

9.752

2.60

**

1.943

2.60

ns

R3

3.210

Full

1.626

R1

15.950

9.809

3.84

**

R2

16.036

9.862

2.60

**

3.299

2.60

**

R3

5.364

Full

1.775

R1

10.766

6.065

3.84

**

R2

18.485

10.414

2.60

**

R3

1.128

0.636

2.60

ns

Full

1.640

R1

4.000

2.439

3.84

ns

R2

22.239

13.560

2.60

**

R3

1.868

1.139

2.60

ns

Full

1.646

R1

3.988

2.423

3.84

ns

R2

32.814

19.936

2.60

**

R3

1.972

1.198

2.60

ns

The pervasiveness of Mobile Data Services 1

97

Results of tests for main effects of geographic location (continued)

Factor A – Geographic location × Factor B – Gender-Prior MDS experience Shortlisted dependent variable

Model

Mean square

Better functionality for games

Full

2.025

Fast access to MDS

Music downloads

Video downloads

Qwerty keyboard

Larger screen

Touch screen

Operating system of mobile phone

**Significant. ns: Not significant. Significant main effect interpreted.

Fobs

Fcritical

Sig.

R1

3.218

1.589

3.84

ns

R2

19.725

9.741

2.60

**

1.130

2.60

ns

R3

2.288

Full

1.599

R1

7.121

4.453

3.84

**

R2

21.651

13.540

2.60

**

2.928

2.60

**

R3

4.682

Full

2.009

R1

45.812

22.803

3.84

**

R2

6.305

3.138

2.60

**

3.252

2.60

**

R3

6.533

Full

1.982

R1

37.808

19.076

3.84

**

R2

5.945

2.999

2.60

**

6.448

2.60

**

R3

12.779

Full

1.958

R1

48.103

24.567

3.84

**

R2

8.512

4.347

2.60

**

5.560

2.60

**

R3

10.884

Full

1.670

R1

18.381

11.007

3.84

**

R2

4.106

2.459

2.60

ns

R3

0.521

0.312

2.60

ns

Full

1.843

R1

14.47

7.851

3.84

**

R2

19.321

10.483

2.60

**

1.612

2.60

ns

R3

2.971

Full

1.848

R1

14.069

7.613

3.84

**

R2

24.483

13.248

2.60

**

R3

4.073

2.204

2.60

ns

98 2

L. Stephanie et al. Results of tests for main effects of gender and prior experience (Singapore)

Factor A – Gender × Factor B – Prior MDS experience Shortlisted dependent variable News

Payment through mobile phones

Location-based services

Stock tracker/alerts

Maps, GPS

Remote monitoring services for elderly

Services to support learning and education

Promotional deals

Fall in prices

Model

Mean Square

Full

1.560

Fobs

Fcritical

Sig.

R1

10.429

R2

0.002

6.685

3.84

**

0.001

3.84

ns

0.794

3.84

ns

R3

1.239

Full

1.816

R1

12.761

7.027

3.84

**

R2

2.55

1.404

3.84

ns

R3

1.599

0.880

3.84

ns

Full

1.645

R1

3.246

1.973

3.84

ns

R2

7.687

4.673

3.84

**

0.218

3.84

ns

R3

0.359

Full

1.642

R1

3.753

2.286

3.84

ns

R2

3.495

2.129

3.84

ns

R3

4.558

2.776

3.84

ns

Full

1.718

R1

2.509

1.460

3.84

ns

R2

14.209

8.271

3.84

**

R3

0.024

0.014

3.84

ns

Full

1.565

R1

3.402

2.173

3.84

ns

R2

6.119

3.910

3.84

**

R3

0.692

0.442

3.84

ns

Full

1.629

R1

1.562

0.960

3.84

ns

R2

7.092

4.354

3.84

**

0.010

3.84

ns

R3

0.016

Full

1.672

R1

1.617

0.967

3.84

ns

R2

7.09

4.240

3.84

**

R3

0.366

0.219

3.84

ns

Full

1.814

R1

2.121

1.169

3.84

ns

R2

10.396

5.731

3.84

**

R3

0.230

0.127

3.84

ns

The pervasiveness of Mobile Data Services 2

99

Results of tests for main effects of gender and prior experience (Singapore) (continued)

Factor A – Gender × Factor B – Prior MDS experience Shortlisted dependent variable Network quality

Network coverage

Availability of more useful services

Ease of use

Better handsets for easier access to wireless services

New communication features

More data-oriented features

Multi-functions

Better overall performance

Model

Mean Square

Full

1.665

Fobs

Fcritical

Sig.

R1

5.100

3.063

3.84

ns

R2

6.921

4.157

3.84

**

0.441

3.84

ns

R3

0.734

Full

1.649

R1

13.523

8.201

3.84

**

R2

5.794

3.514

3.84

ns

R3

2.815

1.707

3.84

ns

Full

1.603

R1

1.599

0.998

3.84

ns

R2

22.833

14.244

3.84

**

R3

1.354

0.845

3.84

ns

Full

1.666

R1

1.133

0.680

3.84

ns

R2

11.503

6.905

3.84

**

R3

1.531

0.919

3.84

ns

Full

1.853

R1

1.949

1.052

3.84

ns

R2

17.183

9.273

3.84

**

R3

0.310

0.167

3.84

ns

Full

1.599

R1

4.028

2.519

3.84

ns

R2

11.889

7.435

3.84

**

1.501

3.84

ns

R3

2.400

Full

1.508

R1

10.964

7.271

3.84

**

R2

21.509

14.263

3.84

**

1.584

3.84

ns

R3

2.388

Full

1.683

R1

10.934

6.497

3.84

**

R2

23.588

14.015

3.84

**

0

3.84

ns

R3

0

Full

1.304

R1

8.224

6.307

3.84

**

R2

3.878

2.974

3.84

ns

R3

4.455

3.416

3.84

ns

100 2

L. Stephanie et al. Results of tests for main effects of gender and prior experience (Singapore) (continued)

Factor A – Gender × Factor B – Prior MDS experience Shortlisted dependent variable Fast access to MDS

Touch screen

Operating system of mobile phone

Model

Mean Square

Full

1.681

Fobs

Fcritical

Sig.

R1 R2

10.103

6.010

3.84

**

11.426

6.797

3.84

**

0.359

3.84

ns

R3

0.604

Full

1.937

R1

8.462

4.369

3.84

**

R2

3.114

1.608

3.84

ns

0.124

3.84

ns

R3

0.241

Full

1.894

R1

9.795

5.172

3.84

**

R2

7.261

3.834

3.84

ns

R3

0.069

0.036

3.84

ns

**Significant. ns: Not significant. Significant main effect interpreted.

3

Results of tests for main effects of gender and prior experience (Los Angeles)

Factor A – Gender × Factor B – Prior MDS experience Shortlisted dependent variable Scheduling

Mobile internet search

News

Mobile banking services

Model

Mean Square

Fobs

Fcritical

Sig.

Full

1.891

R1

1.613

0.853

3.84

ns

R2

15.582

8.240

3.84

**

R3

0.780

0.412

3.84

ns

Full

1.793

R1

1.093

0.610

3.84

ns

R2

37.066

20.673

3.84

**

R3

1.397

0.779

3.84

ns

Full

1.831

R1

7.730

4.222

3.84

**

R2

40.206

21.958

3.84

**

R3

0.596

0.326

3.84

ns

Full

2.004

R1

3.930

1.961

3.84

ns

R2

60.329

30.104

3.84

**

R3

0.032

0.016

3.84

ns

The pervasiveness of Mobile Data Services 3

101

Results of tests for main effects of gender and prior experience (Los Angeles) (continued)

Factor A – Gender × Factor B – Prior MDS experience Shortlisted dependent variable Payment through mobile phones

Location-based services

Stock tracker/alerts

Traffic alerts

Maps, GPS

Model

Mean Square

Full

2.071 0.039

0.019

3.84

ns

59.237

28.603

3.84

**

R3

0.307

0.148

3.84

ns

Full

1.906

R1

3.779

1.983

3.84

ns

R2

36.207

18.996

3.84

**

R3

0.098

0.051

3.84

ns

Full

2.000

R1

6.474

3.237

3.84

ns

R2

49.467

24.734

3.84

**

R3

0.034

0.017

3.84

ns

Full

2.045

R1

1.202

0.588

3.84

ns

R2

11.472

5.610

3.84

**

R3

2.918

1.427

3.84

ns

Full

1.956

R3

0.132 2.243

0.053

3.84

ns

9.530

3.84

**

0.067

3.84

ns

R1

0.471

0.210

3.84

ns

R2

41.462

18.485

3.84

**

R3

1.392

0.621

3.84

ns

Full

2.040

R1

2.530 36.62

R3

5.345

Full

1.934

R1 R2

Games to help children monitor their chronic diseases

0.103 18.64

Full

R2 Services to monitor chronic health diseases

Sig.

R2

R2

Services targeting your ethnic background

Fcritical

R1

R1

GPS tracking services for children

Fobs

5.957 34.71

R3

7.088

Full

1.984

1.240

3.84

ns

17.951

3.84

**

2.620

3.84

ns

3.080

3.84

ns

17.947

3.84

**

3.665

3.84

ns

R1

6.296

3.173

3.84

ns

R2

28.998

14.616

3.84

**

R3

7.482

3.771

3.84

ns

102 3

L. Stephanie et al. Results of tests for main effects of gender and prior experience (Los Angeles) (continued)

Factor A – Gender × Factor B – Prior MDS experience Shortlisted dependent variable

Model

Mean Square

Remote monitoring services for elderly

Full

2.080

Services to support learning and education

Games

Entertainment

Social networking sites

Availability of more useful services

Ease of use

Difficult to use

Complicated

Fobs

Fcritical

Sig.

R1

6.867

3.301

3.84

ns

R2

28.593

13.747

3.84

**

R3

2.971

1.428

3.84

ns

Full

2.097

R1

1.491

0.711

3.84

ns

R2

24.889

11.869

3.84

**

R3

1.730

0.825

3.84

ns

Full

2.063

R1

2.537

1.230

3.84

ns

R2

28.479

13.805

3.84

**

5.410

3.84

**

R3

11.161

Full

2.059

R1

18.583

9.025

3.84

**

R2

40.522

19.680

3.84

**

R3

22.919

11.131

3.84

**

Full

2.178

R1

2.532

1.163

3.84

ns

R2

29.137

13.378

3.84

**

R3

11.972

5.497

3.84

**

Full

1.615

R1

0.081

0.050

3.84

ns

R2

37.526

23.236

3.84

**

R3

0.507

0.314

3.84

ns

Full

1.659

R1

3.037

1.831

3.84

ns

R2

34.887

21.029

3.84

**

1.183

3.84

ns

R3

1.963

Full

2.082

R1

16.218

7.790

3.84

**

R2

0.001

0

3.84

ns

0.240

3.84

ns

R3

0.5

Full

2.150

R1

14.028

6.525

3.84

**

R2

0.563

0.262

3.84

ns

R3

2.647

1.231

3.84

ns

The pervasiveness of Mobile Data Services 3

103

Results of tests for main effects of gender and prior experience (Los Angeles) (continued)

Factor A – Gender × Factor B – Prior MDS experience Shortlisted dependent variable Promotional deals

Model

Mean Square

Full

2.072

R1 R2 Fall in prices

Friends and colleagues use services

Seeing other people using services

Network quality

Network coverage

Better handsets

More modern design

New communication features

3.089 18.16

R3

2.227

Full

1.838

Fobs

Fcritical

Sig.

1.491

3.84

ns

8.764

3.84

**

1.075

3.84

ns

R1

0.193

0.105

3.84

ns

R2

31.288

17.023

3.84

**

R3

0.007

0.004

3.84

ns

Full

1.904

R1

3.967

2.084

3.84

ns

R2

35.422

18.604

3.84

**

R3

1.137

0.597

3.84

ns

Full

1.972

R1

0.818

0.415

3.84

ns

R2

19.618

9.948

3.84

**

R3

0.153

0.078

3.84

ns

Full

1.645

R1

0.029

0.018

3.84

ns

R2

55.163

33.534

3.84

**

R3

0.090

0.055

3.84

ns

Full

1.615

R1

0.008

0.005

3.84

ns

R2

51.067

31.620

3.84

**

R3

0.564

0.349

3.84

ns

Full

1.735

R1

1.107

0.638

3.84

ns

R2

36.521

21.050

3.84

**

R3

4.397

2.534

3.84

ns

Full

1.807

R1

4.589

2.540

3.84

ns

R2

30.529

16.895

3.84

**

R3

0.007

0.004

3.84

ns

Full

1.661

R1

6.187

3.725

3.84

ns

R2

62.118

37.398

3.84

**

R3

0.527

0.317

3.84

ns

104 3

L. Stephanie et al. Results of tests for main effects of gender and prior experience (Los Angeles) (continued)

Factor A – Gender × Factor B – Prior MDS experience Shortlisted dependent variable More data-oriented features

Better functionality for games Multi-functions

Better overall performance

Fast access to MDS

Music downloads

Video downloads

Qwerty keyboard

Larger screen

Model

Mean Square

Full

1.717

Fobs

Fcritical

Sig.

R1

6.381

3.716

3.84

ns

R2

72.871

42.441

3.84

**

0.275

3.84

ns

R3

0.473

Full

2.172

R1

17.799

8.195

3.84

**

R2

46.299

21.316

3.84

**

R3

0.022

0.010

3.84

ns

Full

1.817

R1

8.863

4.878

3.84

**

R2

80.307

44.198

3.84

**

R3

0.013

0.007

3.84

ns

Full

1.396

R1

0.271

0.194

3.84

ns

R2

7.632

5.467

3.84

**

R3

0.029

0.021

3.84

ns

Full

1.558

R1

0.366

0.235

3.84

ns

R2

65.575

42.089

3.84

**

R3

1.245

0.799

3.84

ns

Full

2.128

R1

1.061

0.499

3.84

ns

R2

42.804

20.115

3.84

**

0.426

3.84

ns

R3

0.906

Full

2.131

R1

12.969

6.086

3.84

**

R2

58.093

27.261

3.84

**

R3

3.268

1.534

3.84

ns

Full

1.990

R1

0.964

0.484

3.84

ns

R2

72.590

36.477

3.84

**

R3

0.419

0.211

3.84

ns

Full

1.627

R1

1.529

0.940

3.84

ns

R2

7.790

5.095

3.84

**

R3

1.105

0.723

3.84

ns

The pervasiveness of Mobile Data Services 3

105

Results of tests for main effects of gender and prior experience (Los Angeles) (continued)

Factor A – Gender × Factor B – Prior MDS experience Shortlisted dependent variable Touch screen

Operating system

Battery life

Size and weight

Design

Memory/storage capacity

**Significant. ns: Not significant. Significant main effect interpreted.

Model

Mean Square

Fobs

Fcritical

Sig.

11.025

6.142

3.84

**

49.113

27.361

3.84

**

R3

0.003

0.002

3.84

ns

Full

1.824

Full

1.795

R1 R2

R1

3.495

1.916

3.84

ns

R2

72.566

39.784

3.84

**

0.069

3.84

ns

R3

0.126

Full

1.232

R1

1.015

0.824

3.84

ns

R2

6.693

5.433

3.84

**

0.890

3.84

ns

R3

1.097

Full

1.347

R1

0.967

0.718

3.84

ns

R2

9.688

7.192

3.84

**

R3

0.414

0.307

3.84

ns

Full

1.509

R1

1.627

1.078

3.84

ns

R2

11.294

7.484

3.84

**

R3

0.224

0.148

3.84

ns

Full

1.389

R1

0.291

0.210

3.84

ns

R2

26.493

19.073

3.84

**

R3

0.007

0.005

3.84

ns