2010 a comparison of adoption models for new mobile media services between high and low motive groups

Int. J. Mobile Communications, Vol. 8, No. 5, 2010 487
Copyright © 2010 Inderscience Enterprises Ltd.
A comparison of adoption models for new mobile
media services between high- and low-motive groups
Jong Hyuk Lee*
Department of Journalism and Communication,
Kyung Hee University,
1 Hoegi-dong, Dongdaemun-gu, Seoul 130-701, Korea
E-mail: jonghhhh@khu.ac.kr
*Corresponding author
Joon Ho Kim
Department of Business Administration,
Chung-Ang University,
221 Heukseok-dong, Dongjak-gu, Seoul 156-756, Korea
E-mail: mike0909kim@gmail.com
Jin Hwan Hong
Optimum Management Consulting,
350 Seonreung Acrotel,
140-3 Samsung-dong, Gangnam-gu, Seoul 135-090, Korea
E-mail: jinhongs@naver.com
Abstract: This study aimed to explore what factors influence mobile media
users’ adoption of new services and whether the adoption models differ
depending on users’ motives to use mobile media. The decomposed Theory
of Planned Behaviour (TPB) and uses and gratifications theory were used as
theoretical frameworks. Methodologically, an online survey was conducted
with 400 Korean mobile users and structural equation modelling was
employed. As a result, the proposed model explained well users’ adoption
intention for new mobile services. Especially, high- and low-motive groups
showed significant differences in terms of the influence of subjective norms
and Perceived Behavioural Control (PBC).
Keywords: mobile communication; adoption; planned behaviour; uses and
gratification.
Reference to this paper should be made as follows: Lee, J.H., Kim, J.H. and
Hong, J.H. (2010) ‘A comparison of adoption models for new mobile
media services between high- and low-motive groups’, Int. J. Mobile
Communications, Vol. 8, No. 5, pp.487–506.
Biographical notes: Jong Hyuk Lee is an Assistant Professor in the
Department of Journalism and Communication at Kyung Hee University,
Korea. He earned a Doctoral Degree from Syracuse University and a Master’s
Degree from the University of Missouri-Columbia. His research interests
include journalism, media sociology and new media.

488 J.H. Lee et al.
Joon Ho Kim is a PhD Candidate in the Department of Business
Administration, Chung-Ang University, Korea. He has published papers
in the Korean Management Review, Entrue Journal of Information Technology
and Journal of Engineering Education Research. His areas of research interest
include futures studies and competitive strategy.
Jin Hwan Hong is a CEO of Optimum Management Consulting, and holds
a PhD of Marketing from Chung-Ang University, Korea. His research interests
focus on new product development, marketing strategy and international
marketing.
1 Introduction
An increasing number of people are using mobile media. The world average of
mobile phone penetration reached 49.8% by the end of 2007 (ITU, International
Telecommunication Union, 2009). Korea, the county this study examines, recorded as
high as 93% mobile phone penetration as of 2008 (Asia Today, 2009). Further,
the mobile phone is not only a communication device but also a multimedia content
provider. It allows people to connect to the internet whenever and wherever they want.
Westlund (2007) points out that the mobile phone has become mobile media that
integrates both communication and multimedia content.
Indeed, people surfing the internet and enjoying movies through their mobile phones
while they are on a bus or in the street is not a strange scene. Despite this popularised
use of mobile media, not many studies have provided a clear definition of mobile media.
Lee (2004) considered the mobile media as a portable device of multimedia that allows
a wireless exchange of information and data while users are moving, and creates
unique interactivity and a communicative culture among users. Feldmann (2005)
points out the characteristics of mobile media such as electronic, portable, digitalised
and communicative. A cell phone that allows data exchange and internet access is
a representative example of such mobile media. Notebook computers and portable
receivers of broadcasts are additional examples. However, it is still unclear what range
of media forms belong to mobile media and what functions the mobile media perform.
Thus, this study defines mobile media as “portable and electronic devices that allow
wireless exchange of information and interactive communication”. The form of mobile
media should be ‘portable and electronic’ and the functions that mobile media execute
should be ‘information exchange’ and ‘communication’.
Other than definition of mobile media, many previous research have explored how
people adopt various types of mobile media (Teo and Pok, 2003; Hsu et al., 2008; Li and
McQueen, 2008; Crabbe et al., 2009; Lin and Liu, 2009; Parveen et al., 2009; Wang and
Barnes, 2009; Xu and Yuan, 2009) and what motives drive people to use mobile media
(Höflich and Rössler, 2001). The former research used theories such as diffusion of
innovation, Technology Acceptance Model (TAM), and the TPB while the latter research
mostly relied on the uses and gratifications theory.

A comparison of adoption models for new mobile media services 489
However, little attempt has been made to incorporate both types of research. In other
words, adoption process of mobile media and individual motives of mobile media use
were not examined together. For mobile media users, in reality, many factors such as
perceived usefulness and ease of use (from adoption-related theories) may drive them
to have intention to use new mobile services. However, the whole adoption process may
be affected by what motives they have behind their use of mobile media. We would argue
that incorporation of the motives to the adoption models can explain the adoption process
better. In this sense, this study will divide mobile media users according to their levels of
motives and explore how different their adoption models for new mobile media services
are. Theoretically, this new attempt that deals with motives as a moderating variable
in the adoption-predicting model may fill the gap between adoption-related research
and uses- and gratifications-related research. The results of this study will provide
managerial implications as well. The previous adoption models could show mobile
service providers which factors need to be improved to increase the adoption rate for new
services. In this case, the mobile media users were understood as one homogeneous
group. No segmentation was made. However, mobile media users vary in terms of their
motives to use mobile media. In this sense, this study can provide helpful advices as
to how different adoption strategies should be executed for different mobile media user
groups according to their motives.
After all, this study has three research purposes. First, we will explain how mobile
media users adopt new services and what factors are working in the adoption process.
Second, various motives behind mobile media use will be identified. Third, put together,
we will examine whether the motives play a moderating role in the adoption process of
new mobile media services.
The target audience of this study is mobile media users in Korea, which is one of the
leading countries in terms of the telecommunication industry.1
This study’s findings may
give meaningful implications to other countries and related businesses in the world.
2 Literature review
2.1 Adoption process of mobile media services
Some theories explain what factors influence individuals’ adoption of new media
and how the factors work together: Theory of Reasoned Action (TRA), TAM, TPB and
decomposed TPB.
TRA assumes that individuals’ actual behaviours are influenced by their intention
and the intention is determined by the individuals’ attitudes and subjective norms
(Ajzen and Fishbein, 1973; Fishbein and Ajzen, 1975). Attitude is defined as positive
or negative feelings towards performing the behaviour (Taylor and Todd, 1995) and
subjective norm refers to individuals’ perceptions that people important to them think
they should or should not perform the behaviour (Dillon and Morris, 1996).
On the basis of TRA, TAM developed a conceptual process of new technology
adoption (Davis, 1989; Davis et al., 1989). Individuals’ attitudes are formed on the basis
of perceived usefulness and perceived ease of use. The attitude then influences
behavioural intention and actual behaviour. Unlike TRA, subjective norm is excluded,
and perceived usefulness and perceived ease of use can directly predict intention and
behaviour. Perceived usefulness here refers to individuals’ beliefs that use of new

490 J.H. Lee et al.
technology will be helpful for their performance and perceived ease of use refers to the
belief that the new technology use will not require much effort (Dillon and Morris, 1996).
Recently, TAM has been used to explain the adoption of internet-related technologies,
such as e-mail (Ahn et al., 2004), personal blog system (Shin and Kim, 2008),
e-healthcare (Lanseng and Andreassen, 2007), online taxation systems (Chen and Huang,
2007), e-government (Sahu and Gupta, 2007), online shopping (Ha and Stoel, 2009) and
online banking (Sundarraj and Wu, 2006).
TPB, another development of TRA, suggests that human beings do not have complete
volitional control (Ajzen, 1991). Thus, a new concept, PBC, is added to two existing
factors such as attitude and subjective norms. These three factors predict behavioural
intention and the intention predicts actual behaviour. PBC refers to perceived difficulty of
performing the behaviour.
Reviewing the above-mentioned theories, Taylor and Todd (1995) proposed the
decomposed TPB. It uses some concepts of innovation diffusion literature (e.g., Rogers,
1983; Agarwal and Prasad, 1997) such as antecedents of attitude, subjective norms and
PBC (see Figure 1). This model is known to provide a more complete understanding of
new media use, compared with previous models. The reason is that the unlikely
monolithic belief systems such as attitude, subjective norms and PBC were decomposed
into multi-indicators. Since this model includes more factors than others do, it can
practically demonstrate which specific factors related to new media’s innovativeness and
personal beliefs have significant or insignificant influences on the media use. More
detailed solutions can be provided if any problem is found.
One step further, recently Teo and Pok (2003) provided a modified decomposed
TPB to explain individuals’ use of Wireless Application Protocol (WAP)-enabled mobile
phones. Extending from the above-mentioned research, this study will explore what
factors and how they influence mobile media users’ intention to use new mobile media
services. Not much academic attempt has been made to investigate the process of mobile
media use in general. Few studies have examined what factors and how they influence
mobile media users’ intention to buy new mobile media devices or subscribe to new
mobile media services. On the basis of Taylor and Todd’s (1995) decomposed TPB and
Teo and Pok’s (2003) modified version of it, this study proposes an adoption model for
new mobile media services (see Figure 2) and raises a research question (below). The
proposed model almost represents the decomposed TPB model except for two
modifications. First, actual behaviour predicted by intention was not included because
this study cannot examine actual use of new mobile media services. At the time point of
this study, mobile media users can answer only how likely they adopt new services, not
how often they use them currently, because they did not adopt the services yet. Teo and
Pok’s (2003) study of wireless applications also did not include actual behaviours.
Another modification was to exclude the superior’s influence. Only peer influence
remained. The reason is that mobile media is mostly used personally, not officially.
This indicates that superiors in works rarely influence their subordinates’ use of
mobile media. Rather, peers who are personally close to mobile media users may have
substantial influences on mobile media use. Teo and Pok (2003) also did not measure
superiors’ influences.
RQ1: What factors and how they influence mobile media users’ intention to adopt
new mobile media services?

Figure 1 Decomposed model of Theory of Planned Behaviour
Per_use: Perceived usefulness; Ease_use: Ease of use; Compat: Compatibility;
Peer_inf: Peer influence; Sup_inf: Superior’s influence; Self_eff: Self-efficacy;
Res_fac: Resource facilitation; Tec_fac: Technology facilitation;
Sub_norm: Subjective norm; PBC: Perceived Behavioural Control.
Source: Taylor and Todd (1995)
Figure 2 Proposed model and results of structural equation modelling
*p < 0.05.
**p < 0.01.
Χ2
= 1024.58, df = 386, Χ2
/df = 2.65, CFI = 0.98, RMSEA = 0.06.
Explained variance: 0.77 of Attitude, 0.37 of Sub_norm, 0.57 of PBC, 0.80 of Intent.
Gender (male = 0, female = 1), age, education, and income were controlled for Attitude,
Sub_norm, PBC, and Intent.
Per_use: Perceived usefulness; Ease_use: Ease of use; Compat: Compatibility;
Peer_inf: Peer influence; Self_eff: Self-efficacy; Govment: government;
Sub_norm: Subjective norm; PBC: Perceived Behavioural Control.

492 J.H. Lee et al.
2.2 Motives of mobile media use
The uses and gratifications approach has been used to explore why individuals use certain
media. Apart from the media functionalists’ society-level analysis of the role of media
(Lasswell, 1960), this approach assumes that individual audience members with various
motives actively use certain media to satisfy their needs (Katz et al., 1974; Blumler and
Katz, 1974; Palmgreen, 1984; Rubin, 1994). Many studies in this area have identified
individual motives for various media use: newspapers and magazines (Licheterstein and
Rosenfeld, 1984), free community newspapers (Tsao and Sibley, 2004), television
(Rubin, 1983, 1984), VCR (Lin, 1993), cable TV (LaRose and Atkin, 1988), telephone
(O’Keefe and Sulanowski, 1995), the internet (Roy, 2009), mobile phone (Wei, 2008)
and personal blog (Raacke and Bonds-Raacke, 2008). As far as the identified
motives, Rubin’s (1994) study of television programmes demonstrated information
acquisition, escape, emotional release, companionship, reality exploration and value
reinforcement. Wei (2008) summarised the motives of previous studies into surveillance,
sociability, diversion, escape, arousal, instrumentality, reassurance and companionship.
Traditionally, Katz et al. (1973) five types of motives including cognitive, affective,
interpersonal, social and escaping motives are still considered as a comprehensive
framework. Along the same lines, other studies have suggested two types of
media use: ritualised and instrumental use of media (Rubin, 1984; Metzger and
Flanagin, 2002).
As more and more new media have appeared, this uses and gratifications approach
becomes a helpful framework to understand why audience members decide to use the
new media. Ruggiero (2000) argues “as new technologies present people with more
and more media choices, motivation and satisfaction become even more crucial
components of audience analysis’’ (p.14). Indeed, a number of previous studies have
examined motives for using the internet (Eighmey and McCord, 1998; Fenech, 1998;
Stafford and Stafford, 1998, 2001; Chen and Wells, 1999; Korgaonkar and Wolin, 1999;
Ko et al., 2005; Roy, 2009). For example, the motives found by Papacharissi and
Rubin (2000) were interpersonal utility, pastime, information seeking, convenience
and entertainment.
In the context of mobile media, Höflich and Rössler (2001) identified which motives
encourage German teens to use text messaging: reassurance, sociability, immediate
access, instrumentality and entertainment. Walsh et al. (2007) study of Australian youth
found three gratifications of mobile phone use: self, social and security. However,
not many attempts have been made to explore the motives that are related to the use
of mobile media, overall. Thus, the following research question was proposed:
RQ2: What motives drive individuals to use mobile media?
2.3 Moderating role of motives
Previous studies on TPB have tried to find possible moderating factors in the process of
individuals’ adoptions of new behaviours. Nysveen et al. (2005) compared male and
female groups in terms of TPB process about mobile chat services. Intrinsic motives such
as enjoyment were significant predictors of intention to use the services among female
users, whereas extrinsic motives such as usefulness and expressiveness predicted the
intention among male users. Group identification has been identified as a moderating

factor by many studies on various topics: exercise and sun-protection intention
(Terry and Hogg, 1996), household recycling (Terry et al., 1999), healthy eating
(Åstrøm and Rise, 2001) and binge drinking (Johnston and White, 2003). For instance,
Terry and Hogg (1996) found a significant interaction between group identification
and descriptive norms to predict exercise and sun-protection intention. In both cases, the
effect of descriptive norms on intention was stronger for those who strongly identified
themselves with their groups. Group identification also moderated the effect of PBC on
exercise intention and the effect of attitude on the sun-protection intention. In these cases,
stronger effects were found for those with a low level of group identification. Habit was
another moderating factor in such a way that the attitude–behaviour relationship was
stronger to the extent that habit was weaker (Verplanken et al., 1994). Perceived
confidence and personal values also contributed as moderating variables to predict
behavioural intention in the TPB model (Vermeir and Verbeke, 2006).
Individual motives have been employed in the TPB process as being additional
predictors. Huang’s (2008) study of e-commerce used two theoretical approaches – uses
and gratifications and TPB – and found that the entertainment motive was a significant
determinant of perceived ease of use. Pedersen and Nysveen (2003) included two types
of motives for adoption of mobile parking services: expressiveness and enjoyment.
Expressiveness, a motive to express one’s personality, significantly predicted intention
to use the services. However, enjoyment that was based on four sub-motives such
as entertainment, relaxation, excitement and fun-seeking was not a significant predictor
in the TPB model. Another inclusion of uses and gratifications items to the TPB model
was made by Lee and Kim (2008). They identified three motives for intention to produce
User Created Content (UCC): comfortableness, practical values and information.
Then, the three motives were included in the typical TPB model. All three motives
significantly predicted intention, while only comfortableness and practical values
significantly predicted attitude.
As discussed, motives have been examined as additional predictors in the TPB model.
Almost no study has investigated the possibility of the motives being moderating
factors that make differences in the causal relationships among factors in the TPB model.
Thus, this study will examine whether the level of motives causes any difference in the
TPB-based adoption model for new mobile media services.
RQ3: Does the adoption model, based on decomposed TPB, for new mobile media
services, show any difference between or among user groups of different levels of
motives to user mobile media?
3 Method
An online survey was conducted by a professional research company, Now & Future.
This company with 237,000 panels is one of the largest survey service providers in
Korea. From its panels, 2300 were randomly selected under the rule that the ratios
of gender (male and female) and age (teens, 20s, 30s, 40s, and 50s or older) match those
of the actual Korean population. Of 1140 respondents, 740 who did not use mobile media
were screened out. A total of 400 mobile media users completed this survey, resulting in
a response rate of 17.39%.

494 J.H. Lee et al.
Demographically, half of the respondents were female, while the other half were
male. Various generations were included: 15–19 (9.0%), 20s (23.5%), 30s (26.8%),
40s (23.3%), and 50s or older (17.5%). The majority of respondents (71.8%) were college
graduates and almost half of them (46.3%) were office workers. As for (monthly?)
income, the following ranges were reported: $2000–$3000 (23.5%), $5000 or higher
(19.3%) and $3000–$4000 (17.8%).
In the survey, questions about the decomposed TPB model of mobile media use
and motives to use mobile media were included. Table 1 shows each question’s items
indicating each factor of the model. Questions of Taylor and Todd (1995) and Teo and
Pok (2003) were modified to fit into mobile media usage. All items were included in the
proposed SEM (see Figure 2) and all of them showed 0.71 or higher factor loadings for
corresponding factors. Another criterion of internal consistency among the items,
Cronbach’s Alpha, showed 0.75 or higher for all items of each factor.
A total of 33 questions about motives for mobile media use are shown in Table 2.
An exploratory factor analysis was conducted and five factors with Eigen-values of
1 or greater were extracted. The factors were named as cognitive, affective, interpersonal,
social and comfortable motives. The first factor, cognitive motives, includes seven
questions dealing with needs of news and information. The second factor, affective
motives, consists of nine questions about having fun, escaping from reality, and lessening
distress. Under the third factor, interpersonal motives, eight questions deal with concerns
about personal relationships. The fourth factor, social motives, includes four questions
about making friends. The last factor, comfortable motives, has five questions about
convenient use of mobile media. Four factors except for comfortable motives are similar
to the motives suggested by Katz et al. (1973).
Table 1 Indicators’ loadings in structural equation model
Constructs Indicators (survey questionnaires) Mean (SD) Loadings
Cronbach’s
alpha
MM helps manage my life 3.34 (0.88) 0.89
MM helps complete necessary things 3.36 (0.88) 0.90
Perceived
usefulness
MM helps do my personal things 3.41 (0.89) 0.81
0.90
I use music and movie files easily
through MM
3.76 (0.84) 0.80
I access the internet easily through MM 3.62 (0.84) 0.87
Ease of use
I get necessary information easily
through MM
3.61 (0.79) 0.71
0.83
MM use fits into my life-style 3.27 (0.85) 0.87
MM use matches the way I live 3.27 (0.84) 0.90
Compatibility
MM use goes well with my life 3.28 (0.83) 0.89
0.92
My family influences MM use 3.11 (1.00) 0.84
My friends influence MM use 3.15 (0.97) 0.92
Peer influence
My company colleagues influence MM
use
3.14 (0.96) 0.91
0.92

Table 1 Indicators’ loadings in structural equation model (continued)
Constructs Indicators (survey questionnaires) Mean (SD) Loadings
Cronbach’s
alpha
I am confident in using MM though
nobody taught me how to use it
3.57 (0.84) 0.87
I am confident in using MM though
I never used it
3.54 (0.87) 0.91
Self-efficacy
I can use MM confidently as other
people use it
3.59 (0.86) 0.82
0.90
Government encourages people to
use MM
3.27 (0.83) 0.89Government
Government has a positive policy
toward MM use
3.15 (0.89) 0.81
0.84
Operator MM operators actively encourage
people to use MM
3.65 (0.86) 0.84 0.84
MM operators invest a lot in
advertisements
3.71 (0.87) 0.85
MM use is a good idea 3.66 (0.75) 0.82
It is wise to use MM 3.50 (0.81) 0.83
Attitude
It is pleasant to use MM 3.56 (0.78) 0.79
0.86
People who influence me think
I should use MM
3.11 (0.97) 0.93Subjective
norms
People significant to me think
I should use MM
3.10 (0.97) 0.93
0.93
I have skill enough to use MM 3.65 (0.85) 0.92Perceived
Behavioural
Control
I have knowledge enough to use
MM
3.66 (0.85) 0.91
0.91
I will buy new MM right away
if given a chance
3.38 (0.93) 0.77Intent
I will upgrade my MM services
whenever a new service appears
3.42 (0.88) 0.75
0.75
Table 2 Factor analysis of motives
Survey items
F1
(cognitive)
F2
(affective)
F3
(interpersonal)
F4
(social)
F5
(comfortable)
I use MM
because it is
helpful to get
domestic and
international
information
0.796 0.156 0.119 0.069 0.226
I use MM
because it
provides news
of various
areas
0.775 0.149 0.129 0.049 0.252

496 J.H. Lee et al.
Table 2 Factor analysis of motives (continued)
Survey items
F1
(cognitive)
F2
(affective)
F3
(interpersonal)
F4
(social)
F5
(comfortable)
I use MM
because it is
helpful to get
practical
information
for life
0.754 0.164 0.123 0.140 0.240
I use MM to
search for
information
I am
interested in
0.731 0.151 0.142 0.243 0.241
I use MM
because I can
get in-depth
information
about issues
0.704 0.170 0.249 0.042 0.014
I use MM
because it
provides
credible
information
0.650 0.292 0.264 0.169 –0.076
I use MM
because
I can get
information
that supports
my opinion
0.633 0.251 0.294 0.274 0.157
I use MM
because
I can forget
complicated
things
0.228 0.801 0.255 0.056 0.073
I use MM
because I can
forget work
of my
company or
school
0.157 0.768 0.322 0.056 –0.012
I use MM for
change
0.208 0.730 0.152 0.232 0.186
I use MM
because it is
my hobby
0.207 0.704 0.134 0.221 0.196
I use MM
because I can
be away from
real life
0.190 0.673 0.241 0.152 0.165

Survey items
F1
(cognitive)
F2
(affective)
F3
(interpersonal)
F4
(social)
F5
(comfortable)
I use MM
because it is
helpful in
lessening
distress
0.186 0.646 0.240 0.255 0.253
I use MM
because it
provides
vigour to my
life
0.345 0.555 0.284 0.202 0.340
I use MM
when I have
nothing to do
0.010 0.547 0.125 –0.101 0.443
I use MM
because it is
interesting to
use MM
0.295 0.524 0.231 0.198 0.423
I use MM to
show it off
0.136 0.159 0.893 0.148 0.057
I use MM
because people
envy those
who have
high-tech
products
0.119 0.160 0.881 0.163 0.067
I use MM to
look
fashionable
0.157 0.151 0.878 0.149 0.098
I use MM be
considered as
those who
follow recent
trends
0.174 0.183 0.866 0.117 0.156
I use MM to
socialise with
other people
0.222 0.177 0.791 0.252 0.080
I use MM to
not be behind
other people
0.219 0.331 0.714 0.162 0.108
I use MM
because people
around me use
MM
0.233 0.362 0.659 0.074 0.083
I use MM
because
I am curious
about it
0.201 0.327 0.613 0.099 0.336

498 J.H. Lee et al.
Survey items
F1
(cognitive)
F2
(affective)
F3
(interpersonal)
F4
(social)
F5
(comfortable)
I use MM to
have
conversations
with other
people
0.154 0.222 0.200 0.797 0.141
I use MM to
contact people
I do not meet
often
0.243 0.181 0.303 0.693 0.205
I use MM to
make friends
0.187 0.248 0.489 0.617 –0.058
I use MM to
create
relationships
with new
people
0.417 0.191 0.322 0.493 0.026
I use MM
because I can
use it
immediately
0.121 0.108 0.126 0.051 0.867
I use MM
because I can
use it
anywhere
0.116 0.154 0.177 0.037 0.821
I use MM
because I can
use it at any
time
0.227 0.254 –0.033 0.164 0.639
I use MM
because it
makes my life
comfortable
0.411 0.229 0.097 0.066 0.574
I use MM to
get necessary
information
fast
0.530 0.126 0.123 0.119 0.552
Eigen-value 3.29 2.18 14.3 1.21 1.75
Variance
explained
9.96 6.63 43.40 3.67 5.29
4 Results
The first research question required a test of the proposed model (Figure 2). All variables
in the model are latent factors that have multiple measured indicators, which are not
displayed in the model. Instead, Table 1 shows factor loadings of each indicator for its
corresponding factor and the level of internal consistency (Cronbach’s Alpha) among the

indicators of each factor. All factor loadings were 0.71 or higher and all Cronbach’s
alphas were 0.75 or higher. This indicates that each latent factor has valid indicators.
A SEM was conducted with Amos 7.0 to test the proposed model (Figure 2).
No modification was made and its model-fit turned out to be acceptable level:
Χ2
= 1024.58, df = 386, p < 0.01, Χ2
/df = 2.65, CFI = 0.98, RMSEA = 0.06. Theoretically,
Χ2
value that indicates the distance between a proposed model and actually correlated
model should be small enough for the probability level to be non-significant. However,
Χ2
value is very sensitive to sample size. As sample size increases (generally above 200),
the Χ2
test has a tendency to indicate a significant probability level. Thus, for the study
with large sample size, other model-fit indices should be considered (Schumacker and
Schumacker, 1996). Hu and Bentler’s (1999) recommendation of acceptable level of
model-fit, a widely used criterion in SEM research, was 0.95 or higher of CFI and 0.06 or
lower of RMSEA. On the basis of this criterion, this model’s fitness is acceptable.
Endogenous variables in the model were explained as follows: 77% of the variance in
Attitude, 37% of Subjective Norm, 57% of PBC and 80% of Intent. Regarding the paths
in the model, all predictors showed significant influences on attitude and subjective
norms. However, for PBC, only self-efficacy was a significant predictor. The
government’s and operators’ facilitation of mobile media use did not have significant
influences on PBC.
The second research question aimed to identify motives that drive people to use
mobile media. As noticed, a factor analysis discovered five types of motives: cognitive,
affective, interpersonal, social and comfortable motives (see Table 2). The cognitive
motive refers to the desire to seek and gather information and the affective motive refers
to the desire to relax and be entertained. The interpersonal motive is found in individuals
who want to look good to others by using mobile media. The social motive is found
in those who want to make friends through mobile media use and the comfortable
motive exists in those who use mobile media because it is easy to use in any place and at
any time.
The third research question was about a possibly moderating role of motives in the
model predicting the intent to use new mobile media service. To divide the respondents
according to the level of motives, a two-step cluster analysis was conducted. This method
generates multiple groups based on the change of Schwarz’s Bayesian Criterion (BIC).
It is useful especially when we cannot expect a certain number of groups. As the result,
two groups were identified and one group showed a higher level of motives than the
other group over all five types of motives (see Table 3). Thus, we successfully divided
respondents into a high-motive group (N = 186) and a low-motive group (N = 214).
Next, the structural equation model was tested again for the two groups. A multi-group
analysis technique allows testing of the same model for different groups simultaneously.
Figure 3 shows the results. Model-fit indices showed an acceptable level: Χ2
= 1534.13,
df = 772, Χ2
/df = 1.99, CFI = 0.98, RMSEA = 0.05. In the high-motive group, the
following portions of variances were explained: 85% of attitude, 27% of subjective
norms, 38% of PBC and 76% of intent. In the low-motive group, 49% of attitude, 17% of
subjective norms, 64% of PBC and 66% of intent were explained. Regarding the paths in
the model, in the high-motive group, compatibility was not a significant antecedent
of attitude, and government’s and operators’ facilitations were not significant antecedents
of PBC. Among three predictors of intent, subjective norms were not a significant factor.
In the low-motive group, government’s and operators’ facilitations did not contribute
to explaining PBC positively, and PBC was not a significant predictor of intent.

500 J.H. Lee et al.
Figure 3 Results of multi-group analysis with high-motive group (above) and low-motive group
Table 3 Mean and standard deviation of high-motive group (N = 186) and low-motive group
(N = 214)
Motives High Low t-value
Cognitive 3.98(0.51) 3.09(0.48) 17.70*
Affective 3.90(0.52) 2.99(0.53) 17.18*
Interpersonal 3.50(0.81) 2.52(0.69) 13.08*
Social 3.69(0.67) 2.77(0.59) 14.43*
Comfortable 4.23(0.44) 3.43(0.54) 16.10*
*p < 0.01.

5 Discussion
This study aimed to explain the process of mobile media use, based on the decomposed
TPB, and to discover various motives for the mobile media use. Further, it attempted to
incorporate the motives to the TPB process as a moderating variable. These three
research questions were answered through various analyses of data such as factor
analysis, cluster analysis, SEM and multi-group analysis of SEM.
First of all, this study showed that the decomposed TPB model well explained
the intention to use new mobile media service. In the model (Figure 2), all paths except
for Government PBC and Operator PBC were significant. Further, the model
accounted for 80% of variance in the intention. An interesting point is that among three
predictors of intent, attitude was a more powerful predictor (path coefficient = 0.72)
than subjective norm (0.23) and PBC (0.13). This suggests that individuals should
form favourable feelings towards new mobile media services before deciding to use
the service. Surveying other people’s thoughts and checking one’s ability to control
the new mobile media service could be secondary activities that lead to decision
to use the service. This finding is consistent with that of Taylor and Todd (1995),
which originally proposed the decomposed TPB model. Government’s and operators’
facilitations have not been always significant predictors in previous studies (Taylor
and Todd, 1995; Teo and Pok, 2003). It indicates that PBC is more associated with
self-efficacy. Self-efficacy and facilitation of government or operators may explain
different aspects of PBC because the former is an intrinsic factor whereas the latter
is somewhat an extrinsic factor. Self-efficacy resides in individuals’ personalities and
does not change easily whereas the awareness of government’s or operators’ facilitation
is learned from the environment around individuals and can be changed occasionally.
This study demonstrates that an intrinsic personality-related factor represents the PBC
better than extrinsic environment-dependent factors.
In response to the second research question, we found five types of motives:
cognitive, affective, interpersonal, social and comfortable motives. These are similar to
those motives suggested by Katz et al. (1973), except for the comfortable motive.
It implies that their typology of motives to use media could be true for new media
that keep entering our society nowadays. The other finding based on cluster analysis
shows that mobile media users can be divided into two groups, a high-motive group
and a low-motive group. Individuals who were highly motivated to use mobile media
in terms of one dimension of motives also showed a high level of motives in the other
four dimensions. There was no group that showed high motives in some factors
and low motives in other factors. Actually, mobile media play multiple roles such as
providing news, showing movies and delivering friends’ messages in a convenient way.
Considering this multi-functioning characteristic of mobile media, the users possibly
have all five types of motives with little variation of the extent among different motives.
In the same way, we can see mobile media users who show low motives in all five
motives. Therefore, only two groups of high and low in all five motives were extracted.
The last research question looked at a possibly moderating role of the motives
in the TPB process that predicts individuals’ intention to use new mobile media service.
As in Figure 3, the high-motive and low-motive groups show some similarities and
differences. Regarding the antecedents to attitude, subjective norms, and PBC, most of
them predicted significantly the three factors in both groups. Exceptionally facilitation
variables were not significant predictors. Government’s facilitation predicted PBC

502 J.H. Lee et al.
even negatively in the low-motive group. As noticed earlier, these extrinsic variables are
dependent on the environment and thus easily changeable over time according
to the change in government’s policies or industrial situation. Owing to this unstableness,
the facilitation variables may not be significant predictors. In the high-motive group,
compatibility did not contribute to forming attitude, which was also found in previous
studies (Taylor and Todd, 1995; Teo and Pok, 2003). Two other antecedents of attitude,
perceived usefulness and ease of use, have been found to be significant predictors
in many studies on TAM (Davis, 1989; Davis et al., 1989).
Among three predictors of behavioural intention, attitude showed both a significant
and the strongest influence on the intent in both groups. Even the effect size was
similar between two groups. The path coefficient from attitude to intent was 0.70
for high-motive group and 0.69 for low-motive group. This indicates that individuals’
favourable attitude towards a new mobile media service is the most important factor
that leads to form intention to use the mobile media service. The other two factors,
subjective norms and PBC, showed different influences on intent between two groups.
In the high-motive group, PBC was a significant predictor but subjective norms was not.
In the low-motive group, the opposite occurs. This finding implies that high-motive
individuals do not care about other people’s opinions about their mobile media use when
they decide to use a new mobile media service whereas low-motive individuals rely
on other people’s opinions before they decide to use a new mobile media service.
A plausible explanation for this difference could be based on Hartwick and Barki’s
(1994) study. They proposed two types of information system users, which are mandatory
and voluntary groups. In a model explaining the process of information system use,
voluntary users paid little attention to the opinions of others. Instead, they formed
intentions to use the system because they personally felt that its use would be good,
useful and valuable. In contrast, mandatory users were influenced heavily by normative
components. They formed intentions because they believed important others expected
them to use it. Another interesting finding of Hartwick and Barki (1994) is that subjective
norms are an important determinant of behavioural intention especially in the early stage
when information on new innovation is not enough and therefore potential adopters
have to rely on their referent groups for information. Later, however, as the innovation
gets familiar to many people, the influence of subjective norms becomes weaker
and weaker. The high-motive group is more likely to consist of voluntary users than
mandatory users. Those who are highly motivated to seek information, get entertained,
make friends, and socialise with other people may feel voluntary willingness to use
mobile media. They are not likely to get pressured to use mobile media by other people.
In this light, the high-motive group is not likely to be influenced by subjective norms.
The low-motive groups, when they consider using a new mobile media service, may find
reasons for their use of the service. In this situation, recommendations or advice of people
around them could strongly influence their decision to use the service. Like people in the
early stage of innovation, the low-motive group does not have enough information about
new mobile media service – indeed, they are not motivated to seek information – and
therefore they may have to rely on other people’s opinions before deciding to use the new
service.
One contribution of this study lies in the examination of the process of mobile
media use with motives as a moderating variable. However, future studies need to further
explore the role of motives. For example, two models including motives as a mediating
variable and as a moderating variable can be compared to figure out which one represents

reality better. Finding more variables that influence the process of mobile media use
will be needed in the future.
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1
According to ITU’s Information and Communication Technologies (ICTs) development index,
Korea ranked second among 154 countries in the world. This index combined 11 indicators such
as ICT access, use and skills, the number of internet users and literacy levels.

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