Mobile service usage behavior in Korea: an empirical study on consumer acceptance of innovative technologies/Mobiliojo rysio paslaugu naudojimas korejoje: empirinis tyrimas apie inovatyviu technologiju pripazinima tarp vartotoju.
Sawng, Yeong-Wha ; Kim, Seung-Ho ; Lee, Jungmann 等
1. Introduction
The Korean mobile service industry, after starting out with
services targeting 1st-gneration analog cellular devices, has since then
undergone rapid expansion and is today supplying to 3rd-generation
device markets such as WCDMA and HSDPA. As a result of technological
progress and improvement in income, consumers' needs in technology
products and services have been fast evolving in recent years, and this
has, in turn, caused the market environment to change rapidly. In
December 2007, the Korean mobile service market passed the 45 million
subscriber mark, and its 3G segment reached over 10 million subscribers.
3G mobile services currently in offer in Korea are considered to have
largely surpassed 2G services in terms of network efficiency and service
capacity, outperforming them by more than 30% in both areas. 3G services
are also optimally adapted for data communications. With 3G services,
mobile consumers have not only a seamless access to all traditional
internet content, such as movies and music, but also can use data-heavy
applications such as video phone. Meanwhile, the voice-centered mobile
service market is quickly reaching the point of saturation with the
potential for further increasing revenue being all but exhausted. 3G
services, as a new alternative more adapted for transfer of data and
multimedia content, are expected to offer new revenue opportunities for
the mobile market. In spite of this exponential growth of mobile
services and leaps and bounds made in related technology, few attempts
have been thus far made to understand consumers' usage behavior
with regard to these services. The existing literature on this topic is
scanty, especially if compared to the wealth of studies dealing with
information systems and web services.
The main purpose of this study is to investigate consumers'
usage behavior in mobile services to provide pointers for future
development of this field. Specifically, this study attempts to identify
variables influencing the use of mobile service consumers.
We developed an information technology acceptance model to
determine usage characteristics and usage trends in mobile services and
predict consumers' usage behavior based on patterns discerned from
these characteristics and trends. Information technology acceptance
models are a useful tool for understanding the behavior of consumers of
services such as mobile services, combining aspects of a technology and
service good, from the perspective of behavioral science. This study,
comprehensively concerned with mobile service marketing- related issues,
considers both factors influencing consumers' usage behavior and
also technical aspects of services. Factors influencing the behavior of
mobile service consumers are investigated empirically, by examining them
in a real-world context.
The expected outcomes of this study are as follows: first, it will
predict consumers' usage behavior with regard to mobile services
based on usage characteristics at the level of task management, and
discern identifiable and recurring patterns. Second, it will awaken
providers to the importance of technical and functional performance and
a clear understanding of consumers' needs for the success of
marketing mobile services.
To achieve these research goals, we empirically estimate the
relationship between characteristics related to the use and provision of
mobile services and factors influencing consumers' satisfaction
such as perceived benefit and risk. Four constructs related to usage
characteristics were selected as measured variables; namely, perceived
usefulness, perceived ease of use, perceived cost and perceived network
effects. Perceived benefit variables include economic benefits,
service-specific benefits and social benefits, and perceived risk
variables, service risk and cost risk. The rest of this paper is
organized as follows: In Section II, we review existing literature on
the process of individual's acceptance of information technology,
examining more particularly theoretical models such as the diffusion of
innovations theory, information technology acceptance models, technology
task fit model and perceived risk theories. In Section III, we describe
the research model developed for this study, drawing on the existing
literature, and formulate hypotheses on the relationship between the
variables. Finally, in Section IV, the paths of influence between the
variables are empirically estimated using a structural equation model.
2. Theoretical Background
2.1. Literature on Information Technology Acceptance
2.1.1. Diffusion of Innovations Theory
Rogers (1995) distinguishes five stages in the process by which an
individual comes to adopt an innovation: knowledge, persuasion,
decision, implementation, and confirmation (see Fig. 1). The stage of
knowledge corresponds to the initial phase in which an individual is
first exposed to an innovation and develops an understanding about how
this innovation works. The process by which an individual develops
knowledge related to an innovation is largely influenced by his/her
personal taste, needs and desires, and past experiences, as well as the
values and norms of the social systems (Weiber 1992, 1994). The
knowledge developed during this stage serves the potential adopter of
the innovation as the basis to form his/her opinion about the innovation
in question. In other words, during the stage of persuasion, the
potential adopter forms either a favorable or unfavorable attitude
toward the innovation, based on the knowledge he/she has gained from the
initial knowledge stage. While the individual's mental activity
during the knowledge stage is mostly cognitive in nature, during the
persuasion stage, it is predominantly affective. As a general rule, the
easier it is to observe the results of an innovation and to communicate
them to others, the higher the probability that the potential adopter
will actually choose this innovation (Rogers 1995). This decision
whether to adopt or reject the innovation is made during the third
stage, and the attitude developed during the persuasion stage provides
the basis for decision-making. According to Rogers (1995), most
individuals are willing to try out an innovation before forming an
opinion. The intention to adopt an innovation is usually dependent on
whether or not the potential adopter sees relative benefits or
advantages associated with the adoption of the innovation. It is during
the fourth stage, in other words, the stage of implementation that the
final decision is made on whether to adopt or reject the innovation. The
implementation, therefore, occurs when an individual reaches the final
decision to use the innovation. During the implementation stage, most
individuals keep searching information about the innovation, as some
degree of uncertainty on the potential consequences of using this
innovation still lingers. Finally, during the stage of confirmation, an
individual, based on the level of satisfaction he/she experiences with
regard to the results of using the innovation, considers whether or not
to continue to use the innovation. A satisfied innovation adopter would
typically choose the same product at the next purchase occasion
(Borchert et al. 2004).
[FIGURE 1 OMITTED]
The first three of the five stages in the innovation decision
process are each related to 'belief,' 'attitude' and
' intention' in the technology acceptance model (TAM), and
provide the basis for combining the diffusion of innovations theory with
TAM. Benbasat and Dexter (1992) developed a method for measuring
constructs believed to influence the adoption and acceptance of
innovations, based on the diffusion of innovations theory and related
literature. Using this method, he measured the extent to which these
factors actually influence individuals' use of information
technology. Meanwhile, 'perceived risk', a concept proposed by
Rogers in the context of potential innovation adopters' assessment
of the relative quality of a product, has been reported to be one of the
important reasons why customers are reticent about e-commerce (Schmalen
and Pechtl 1996). Borchert et al. (2004) elaborated a new model to
measure the effects of various factors deemed to influence buyer
behavior on the frequency and value of purchase, drawing on the
diffusion of innovations theory and marketing literature on related
topics. His analysis found that four factors, including internet usage,
search capacity, confidence in the seller and confidence in the security
of transactions over the internet had a positive influence on the
frequency and value of purchase. Regarding mobile services specifically,
consumers' knowledge and experience of the internet are believed to
influence their perceived ease of use (Hoffman et al. 1995). Prior
experience proves to be crucial in the acceptance of innovations,
insofar as an innovation which appears too new or unfamiliar or whose
use requires extensive changes in consumers' past habits tends to
increase the barrier to acceptance. The level of acceptance is generally
lower with innovations based on an entirely new body of knowledge, in
other words, innovations with which consumers have little prior
experience. This is also the reason why with innovative products or
services new to the market, those consumers who are able to understand
their innovative characteristics and assess them appropriately, tend to
accept them earlier than others (Gerpott 1999). In addition to
consumers' knowledge of computer and the internet, the perceived
ease of use with regard to a mobile service is often influenced also by
the familiarity of the software and ease of its installation (Doring
2002). Perceived familiarity, therefore, is an essential requirement for
the market success of a new technology or service (Hoffman et al. 1995).
In other words, the mode of network access should be familiar to mobile
service consumers, the interface, easy and convenient to use and search
functions more powerful (Harms 2002).
In this study, to determine innovation acceptance characteristics
of mobile service consumers, we begin by examining possible reasons why
a consumer accepts or rejects a new information technology. This choice
is based on the likelihood that the benefits consumers expect from an
innovation play a determining role in their acceptance of it (Reichwald
1982). The knowledge of why consumers accept or reject a mobile service,
furthermore, can assist service providers in designing marketing
strategies which closely takes into consideration innovation acceptance
characteristics of consumers. Needless to say, achieving rapid
acceptance by consumers is vital for the market prospects of a new
mobile commerce service (Kollman 1998, 2004).
2.1.2. Technology Acceptance Model
The TAM (Technology Acceptance Model), developed by Davis et al.
(1989) (see Fig. 2), is a model widely used to explain and predict
consumer behavior with regard to the acceptance of information
technology.
[FIGURE 2 OMITTED]
An extension of Fishbein and Ajzen's (1975) theory of reasoned
action (TRA), the TAM explains how consumers come to accept and use a
technology. In his original study, Davis (1989) sketched out basic
relationships between cognitive and affective variables influencing the
acceptance behavior of a technology consumer, using the conceptual tools
provided by the theory of reasoned action. In the TAM, acceptance
behavior is determined by two beliefrelated factors, 'perceived
usefulness' and 'perceived ease of use,' and whether an
information technology would be ultimately accepted by its potential
consumers is predicted using the belief--attitude- intention--behavior
sequence proposed in the theory of reasoned action. Perceived usefulness
and perceived ease of use, as the two key belief factors shaping the
attitude, intention and behavior of a technology consumer, are believed
to lead to the acceptance of a new information technology (Lederer et
al. 2000). Notwithstanding, in some cases, the influence of perceived
ease of use on the acceptance of a technology can be negligible or
difficult to prove, and this phenomenon is explained by the fact that
the relative importance of influence factors for technology acceptance
varies depending on the type and characteristics of the task for which a
new technology is used (Neudorfer 2004). Previous studies found that
perceived ease of use had a greater influence on the acceptance behavior
of technology consumers, than usage characteristics related to using or
purchasing high-quality information services or products, if and when a
technology is employed for its originally-intended purposes, in other
words, to use an information system (ex. searching educational
materials, etc.) (Gefen and Staub 2000). Accordingly, in this attempt to
measure the impacts of various factors influencing the acceptance
behavior of mobile service consumers, our primary focus is placed on the
consumer perception of security and reliability.
2.1.3. Task Technology Fit Model
The task technology fit model (TTFM) is a tool to assess the extent
to which an information system supports the tasks carried out by
potential consumers. This model, developed by Goodhue and Thompson
(1995), has been popularly employed in the context of research to gauge
the satisfaction and attitude of consumers with regard to information
systems, as well as MIS research. The task technology fit model is
considered a pertinent tool for evaluating the quality of an information
system and predicting how much an information system would be actually
used by potential consumers. Although this model has been thus far used
mostly as a secondary tool, at least within the context of acceptance
research, given its effectiveness in evaluating service providers'
capability to address consumers' needs at the level of information
systems, it could very well be used as an alternative explanatory model
for acceptance behavior. This is because the acceptance of a technology
is susceptible to general factors such as the fit between information
systems, tasks, technologies and individual consumers (Goodhue and
Thompson 1995). Dishaw and Strong (1998), in their investigation of the
TTFM, stated that one of the model's postulates is that software
will be used if, and only if, the functions provided by it support the
tasks performed by the consumer. In other words, a consumer with the
habit of rational thinking and prior experience using information
systems is bound to choose a tool that enables him/her to successfully
perform a task, delivering maximum effectiveness at a minimal cost. The
influence variables in the TTFM, including technology, task and fit, are
defined as follows (Amberg et al. 2003): first, technology is regarded
in the TTFM as a tool supporting individual consumers' activities
(Goodhue and Thompson 1995). Technology, in other words, is a support
service assisting both the computer system and the consumer with the
performance of his/her primary tasks. The term 'technology,'
as used in the TTFM, has a broad and rather general meaning. This is
because technology not only designates concrete systems, but also refers
to all influences that information systems may have on potential
consumers and their activities. Second, tasks are understood as all
activities through which an input is converted into an output. One thing
to be noted in regards to tasks in the context of TTF is that with an
interesting task, the consumer relies more extensively on information
technology. Individuals are agents who use technologies that can assist
them with the performance of their respective tasks, and individual
characteristics (amount of training and experience with using computer
and motivation, etc.) influence how easily a consumer learns to use a
new technology. Third, 'fit,' the pivotal concept in the TTFM,
is a variable closely related to the consumer's experience of
satisfaction.
To enable quantitative measurement, Goodhue and Thompson
distinguished twelve dimensions of task-technology fit: accessibility,
assistance, ease of use, system reliability, data accuracy,
compatibility, currency, presentation, confusion, the level of detail,
meaning and locatability. A higher level of task-technology fit
increases the consumers' expectations on the results and allows
him/her to perform a higher-level task as well (Goodhue and Thompson
1995). In this study, we assume that the three influencing factors in
the TTFM are at work also in the process of acceptance of mobile
services, and will use this model to evaluate the fit between a mobile
service and its potential consumers (Amberg et al. 2003).
2.1.4. Theory of Perceived Risk
The concept of perceived risk was first proposed by Bauer (1960).
Bauer stated that the consequences of a choice made by a consumer can be
unpredictable and some of these unforeseen consequences can be
undesirable. Perceived risks refer to the potential risks of a purchase
decision as perceived by consumers. Perceived risks, meanwhile, should
be distinguished from objective or statistical risks, insofar as
consumers only respond to subjectively-perceived risks, and such
subjective risks perceived by a consumer may or may not prove real. Cox
(1967) explained perceived risk by two factors: the perceived extent of
loss that may be incurred from an unwanted outcome of a purchase
decision and the perceived amount of security that the purchase would
not lead to an unexpected outcome. These two concepts provide a basis
for risk reduction strategies to decrease the possible loss and increase
the level of security to avoid undesirable consequences (Neudorfer
2004). As for Bettman (1973), he further elaborated the concept of
perceived risk by distinguishing two types: inherent risk and handled
risk. Inherent risk is the latent risk a product class holds for a
consumer, in other words, the innate degree of conflict a product class
can arouse in a consumer. Handled risk, on the other hand, represents
the conflict potential which still exists when choosing a brand from a
product class at the moment of the purchase decision, and may,
therefore, be described as the 'perceived residual risk.'
Using the distinction between these two components, Bettman measures the
risk-reduction behavior of consumers (Neudorfer 2004). Meanwhile, Cox
(1967) states that the realization that the goals of purchase may not be
reached arouses anxiety in consumers, and the perception of risk stems
from the consumer's uncertainty about his/her own goals of
purchase, whether a product corresponds to the goals of purchase (if the
goals are known), or whether he/she will be satisfied with the
consequences of the purchase. In other words, perceived risks are
negative consequences perceived by a consumer in relation to a purchase
decision, when he or she is unsure about the goals of his/her own
purchase, which product or model corresponds best to the goals of
purchase or whether he/she will be satisfied with a product, after the
product is bought. Cunningham (1967) classified perceived risks in a
purchase decision into six types: financial loss, physical loss,
psychological loss, time loss, social risk and performance risk.
Perceived risk in a purchase decision is determined by negative purchase
consequences and insecurity (see Fig. 3). Insecurity is closely related
to the possibility envisioned by a consumer that the goals of his/her
purchase may not be achieved, and the consequences of a purchase include
the consequences of a purchase decision. The theory of perceived risk,
therefore, maintains that a behavioral pattern can be extrapolated from
how a consumer handles the risk they perceive in a purchase decision.
According to their attitude toward risk taking, in other words, their
level of willingness to accept a risk, consumers can be classified into
three groups: risk-friendly, risk-averse and risk-neutral consumers
(Neudorfer 2004). In order to measure perceived risk with a reasonable
degree of accuracy, using constructs such as negative purchase
consequences and insecurity, these constructs must be appropriately
adjusted. For these constructs to be used in the analysis of consumer
behavior in advanced and complicated technology products such as mobile
services, these perceived risk factors need to be clearly defined
(Weiber 1994).
[FIGURE 3 OMITTED]
2.2. Concept of Mobile Service and Literature Review
Mobile commerce services allow their consumers to exchange values
and conduct business transactions over wireless networks, using portable
devices. It is a superior alternative to ecommerce in that transactions
can be conducted at any time and from anywhere (Villanen et al. 2004).
Although similar to e-commerce insofar it uses the internet, mobile
commerce is distinct from the latter in terms of enabling technologies
and business models. As devices used for accessing wireless internet are
portable devices limited in data storage capacity and interface
functionality, the medium emphasizes network-centered mobility and is
evolving into a platform which is more personalized than mobile internet
with service providers taking up an active role in resolving temporal
and spatial constraints experienced by consumers (Turowski and Pousttch
2004). On the consumer side, mobile internet is also distinct from fixed
internet access, as it does not offer extended hours of search and free
access to information. The duration of connection is shorter with mobile
internet, and access to information and services is on a pay-for-use
basis (Buse 2002). Neudorfer (2004), in his study investigating factors
influencing the acceptance of mobile services, found that expected
benefits had a positive influence on the probability of adoption.
Meanwhile, his investigation found that perceived risk (service risk and
cost risk) had no significant influence on consumers' acceptance of
mobile services. Amberg et al. (2003) measured customers'
satisfaction with mobile services, before and after the actual
experience of the services, and found that usefulness, ease-of-use, cost
and mobility were factors effectively influencing consumers'
attitude toward a mobile service both during and after the use. Pleasure
experienced using text services and their perceived usefulness and
usability have been also reported to have an influence on the level of
consumer satisfaction with mobile services (Doring 2002). Lehner (2003),
in his study on the characteristics of mobile content interface, content
services and mobile internet usage, found that convenience and
efficiency were the two most important factors influencing the
acceptance of mobile services. According to his results, at the level of
content interface, structural simplicity and ease of search effectively
influenced the acceptance behavior of mobile service consumers.
Meanwhile, he measured variables including accuracy, timeliness,
personalization, specialization, locatability and simplicity to
characterize mobile service providers. Bullinger and Schreiner (2003)
investigated mobile internet users' behavioral intention using the
TAM and found that the quality of the system and information influenced
perceived pleasure, ease of use and usefulness.
3. Research Model and Hypotheses
3.1. Research Model
Drawing on theoretical and empirical literature on consumer
behavior and the acceptance of information technology, we created a
conceptual model to explain consumers' usage behavior in mobile
services. Based on constructs influencing technology acceptance from
previous studies and relationships that are reported to exist between
them, we investigated how the various usage characteristics in mobile
services influence each other (see Table 1).
The above attempts at explaining the mechanism of diffusion of
innovations are variously limited by their failure to clearly account
for the role of perceived risk or product attributes or provide rational
explanations to consumers' acceptance behavior. Further, the set of
factors influencing consumers' acceptance of innovations considered
is either outdated or incomplete, as most investigated cases of failed
innovations date from some decades ago. The explanatory capacity of the
theory of perceived risk is also limited, as it falls short of
explaining the extent to which various risk factors influence
consumers' acceptance behavior or whether there are certain
acceptance factors which counter the effect of any of the risk factors.
Expanding on the existing literature on consumer behavior, we
investigate consumers' usage behavior in mobile services by
examining the interaction between the different factors of influence
delineated in the research model in Fig. 4. We begin by constructing a
theoretical model for explaining consumers' usage behavior in
mobile services and formulating hypotheses about various influence
paths. Next, we select variables for each of the constructs and set up
hypotheses about relationships of influence that may exist between them.
[FIGURE 4 OMITTED]
3.2. Hypotheses
3.2.1. Relationship between Expected Benefits of Mobile Services
and Acceptance Characteristics
According to Neudorfer (2004), both an increase in expected
benefits and the process of handling perceived risk can heighten the
adoption probability for a mobile service. Neudorfer measured expected
benefits of mobile services, classifying dimensions in which benefits
can be created and perceived into three categories: economic benefits,
service-specific benefits and social benefits. Economic benefits, the
most important benefits according to Neudorfer, include benefits arising
from time-saving, as time saved may have economic consequences for the
consumer, even if it cannot fully or appropriately translated in
monetary terms. Hence, both money saved and times saved are considered
economic dimensions in the context of the adoption of a mobile service.
Service-specific benefits are potential benefits arising from the use of
a mobile service application. Given the great diversity of mobile
services offered, qualities related to mobility of mobile services
(ubiquity, reachability, personalization, localizability, etc.) are
classified into two broad dimensions, for simplicity's sake: time
independence and place independence (Amberg et al. 2003). Social
benefits, although a category of benefit difficult to quantify, are
defined as benefits arising from social interaction through cyber
communities. The mobile media allow people to reach beyond the
traditional, geographically-limited social sphere to enter into contact
with others in remote locations.
Inter-human benefits gained from communicating with people of
similar interest through email would be a good example of social
benefits. Social benefits are broadly defined as including fun,
enjoyment and entertainment associated with social interaction through
mobile media. Based on the above benefit constructs, we set up the
following three hypotheses concerning the relationship between perceived
benefits and the acceptance of a mobile service:
H 1-1: Service-specific benefits have a positive influence on the
acceptance of an m-service and the degree of satisfaction felt by
consumers.
H 1-2: Social benefits have a positive influence on the acceptance
of an m-service and the degree of satisfaction felt by consumers.
H 1-3: Economic benefits have a positive influence on the
acceptance of an m-service and the degree of satisfaction felt by
consumers.
3.2.2. Relationship between Perceived Risk Factors and Acceptance
Characteristics
Perceived risk is a component of a consumer's purchase
behavior having to do with the prospect of reaching the goals of a
purchase, which plays a determining role in the purchase decision. In
this study, following the existing literature, we distinguished two
types of perceived risk: service risk and cost risk. Service risk is
related to the innovation characteristics of a particular technology,
and the technology, in turn, affects the outcome of a purchase.
Potential benefits of an innovative service and technology are important
criteria of consideration for consumers, in a purchase decision.
Consumers who have negative perceptions of a technology's
innovation characteristics tend also to perceive the outcome of their
purchase as risky. Negative perceptions of innovation characteristics
would lead consumers to form negative opinions about the quality of
components of a mobile service, or the capacity or reliability of the
service provider. Consumers' negative perceptions about the outcome
of their purchase means the low level of acceptance for the technology
in question, as they indicate that their perceptions of integration
capacity and ease of use are also negative. Hence, service risk may be
defined as a risk related to insecurity. Cost risk has to do with market
and provider-related purchase consequences, and concerns primarily the
change in costs and cost assessment. Negative market-related purchase
consequences may be a sharp decrease in the price of the service or a
better alternative becoming available after the purchase. Negative
provider-related purchase consequences include inadequate installation
support, the absence of free repair service, a long waiting time for
service and provider-supplied information proving inaccurate; all of
which may result in additional costs for consumers. The perceived risk
of an innovation and the assessment of this risk have a direct influence
on consumers' purchase behavior. Acceptance characteristics and
consumers' purchase behavior with regard to an innovation are
determined by the process of adoption. As the adoption process for an
information technology is rather long and complicated, in order to use
appropriate risk reduction strategies, one has to empirically measure
the extent of influence of perceived risk. The acceptance
characteristics of an innovation and consumers' purchase behavior
toward it are also influenced to a degree by the amount of insecurity.
It is, therefore, important to increase the security of transaction for
consumers making a purchase decision, by providing more information
regarding goal-related results (Neudorfer 2004). Concerning the
relationship between the perceived risk and acceptance of a mobile
service, we set up the following two hypotheses:
H 2-1: The service risk associated with an m-service has a negative
influence on its acceptance and the degree of satisfaction felt by
consumers.
H 2-2: The cost risk associated with an m-service has a negative
influence on its acceptance and the degree of satisfaction felt by
consumers.
3.2.3. Relationship between Mobile Service Usage Characteristics
and Satisfaction
The characteristics of consumer acceptance are considered
increasingly important in the evaluation of mobile services as well as
in general research on this topic. This is because acceptance
characteristics are highly meaningful, predictive indicators for
consumers' usage behavior with mobile services (Amberg et al.
2003). To measure the characteristics of consumers' acceptance of
mobile services, they developed a new model, called the "Compass
Acceptance Model", expanding the original TAM model in which the
acceptance of an innovative technology is determined by two generalized
determinants, perceived usefulness and perceived ease of use. Their
measurement of consumer acceptance characteristics in mobile services
takes into consideration a variety of practical factors pertaining to
common service situations and conditions. The traditional TAM model was
reconstructed by adding new factors of influence, such as perceived
costs and perceived network effects, which allowed them to measure more
comprehensively the characteristics of consumers' acceptance of
mobile services at different levels. Meanwhile, Silberer et al. (2002)
included customers' satisfaction with mobile services applications
as a measurement concept; something never previously done in studies
comparing the TAM with other technology acceptance models (e.g. Goodhue,
Egenhardt, Kollman). He measured this acceptance construct by examining
hardware characteristics, the characteristics of transmissions costs and
characteristics of m-commerce, as well as the characteristics of
experience and correspondence to expectations.
Table 2 provides the list of constructs from existing literature,
related to the characteristics of consumer acceptance with regard to
mobile services. These constructs are appropriately redefined and
reorganized to suit the purpose of this study. The constructs have the
following relationships of influence between them: First, factors of
influence related to perceived usefulness are factors having to do with
value-added, emotional rewards and information quality. Second,
perceived ease of use-related factors include experience at the initial
operation, the usability of service and usability of terminal equipment.
Third, perceived cost-related influence factors are monetary costs,
transparency and health concerns. Finally, perceived network effects
capture the general status of a mobile service and have an indirect
influence on its acceptance. The network coverage of a service and the
design, size and color of the monitor or display belong to this category
of perceived product attributes. We set up one hypothesis about the
relationship between consumer acceptance characteristics and
satisfaction as follows:
H 3: The characteristics of consumer acceptance of m-services have
a positive influence on satisfaction.
4. Methodology and Empirical Analysis
4.1. Data Collection and Sample Characteristics
A survey was conducted to collect the data needed to test the
hypotheses advanced in this study. The target population of the survey
was college students, as they represent the majority of mobile service
consumers. The respondents were chosen among college students attending
a Gyeonggi-area university and another university located in the Daejeon
area. Working adults attending night schools made up a sizeable
subgroup, whose age and occupation distributions are more diverse than
the rest of the sample population. Thanks to its diversity, this
subgroup plays the important role of reducing the influence of
demographic variables. The survey was conducted on 250 respondents, and
197 responses were retained for analysis. The sample size is limited,
but the extensive experience of members of this subgroup with various
information technology media, including fixed internet and m-commerce,
and their proficiency with wireless devices also speaks favorably about
the appropriateness of this choice of target population to our purpose.
The respondents break down as follows, by age group, education, gender,
average monthly income and monthly average length of mobile service
usage: people in their teens accounted for 31% of total respondents,
people aged 20 to 29. 66% and people aged 30 and older, 3%. All
respondents were enrolled in a college or university at the time of the
survey, the sample population being college/university students. By
gender, the number of women slightly exceeded that of men, representing
56% of total respondents. The average monthly income (allowance) of the
sample ranged from 100.000 won to 400.000 won, and the distribution of
the average monthly expenses also ranged between 100.000 won and 400.000
won. The average hours of mobile phone use per day, for calls, amounted
to 2.7 hours for both men and women, and the monthly average cost, about
50.000 won. For services other than calls, the respondents used their
mobile handsets over three times a day on average, to send text
messages, look up information, play games and download mp3 music. These
days, most cell phones plans include over 100 free text messages a month
at no additional charges. There was no discernable difference between
men and women in the usage of text messaging.
4.2. Reliability and Validity Analysis
To measure the extent to which known antecedents of the acceptance
of mobile services actually influence consumers' acceptance
behavior and their level of satisfaction, we employed a variety of
analytical methods. First, the internal consistency of constructs was
tested using Cronbach's Alpha, and any measurement items which
proved unreliable were discarded. Second, a factor analysis was
performed to test the construct validity of the measures. Third, a
correlation analysis was performed on the remaining items to check for
any correlations between the variables. Fourth and lastly, we performed
a path analysis to estimate the research model by testing the
measurement model and the structural model separately.
4.2.1. Reliability Analysis
In this study, constructs drawn from previous research were given
operational definitions, and measured through multiple-item scales and a
7-point Likert-type response format. Cronbach's Alpha coefficient
was used for internal reliability analysis, and items of low reliability
were excluded from the analysis. The reliability test performed on the
variables retained for the analysis (see Table 3) after exclusion of
these items resulted in a Cronbach's Alpha coefficient to 0.7 or
higher for all scales; a level indicating a sufficient degree of
reliability.
4.2.2. Validity Analysis
With regard to the validity of the items used in this study, the
convergent validity of the instruments, indicative of the internal
consistency between the items, can be measured by construct reliability.
Discriminant validity, meanwhile, indicates to what extent the results
of two measurements of the same attribute, using two different methods
are related or coincide with each other (Campbell & Fiske 1959). In
this study, the discriminant validity of the measures was tested by
factor analysis. To minimize information loss and reduce the number of
factors for each variable to the smallest possible number, we used
principal component analysis. The analysis was performed using Varimax
rotation, an orthogonal rotation which maximizes the variance of the
factor loadings. The orthogonally rotated factor matrix for the
principal component analysis to test the construct validity of
independent variables is displayed in Table 4. The results of the factor
analysis show that there were four factors that could be extracted at
the 'service characteristics' level. Factor loadings from the
principal component analysis were 0.5 or higher for all items, with
communality estimates of 0.5 or higher.
The factor analysis on the perceived benefits and risks of mobile
services was also performed. The results like 'service
characteristics' level, although diverging from the initial
expectations, indicate, nevertheless, a satisfactory level of
discriminant validity between the independent variables.
4.2.3. Confirmatory Factor Analysis
Confirmatory factor analysis is used to test specific hypotheses
for the extent to which they explain the interrelationships observed
between the data. Hypotheses are formulated either based on prior
knowledge or theoretical results, and restrictions may be imposed on the
values of certain variables. Confirmatory factor analysis is performed
either when exploratory research has been already completed, or when
there exists a hypothetical but plausible instrument, to validate the
assumptions (Kim 2001). In this study, we used the statistical package
AMOS 4.0 for confirmatory factor analysis. The statistical package
enables the analysis of complex causal relationships through covariance
structural modeling.
To evaluate the validity of the constructs, we used model fit
indices including [x.sup.2] (the smaller, the better) including p-value
(the optimal value is 0.5 or lower), RMR (Root Mean Square Residual;
best if 0.05 or lower), GFI (Goodness-of-Fit; best if 0.9 or higher),
AGFI (Adjusted Goodness-of-Fit; best if 0.90 or higher) and NFI (Normed
Fit Index; best if 0.90 or higher). The results of the confirmatory
factor analysis are listed in Table 5.
The results of the confirmatory factor analysis proved to be
generally satisfactory, although the [x.sup.2] values for perceived ease
of use and social benefits, and the p-values for perceived costs,
economic benefits and cost risk exceeded the cut-off values (P <
0.5). However, the RMR values were close to 0, and the GFI values,
corresponding to the degree of the model fit, were above 0.9 for all
constructs. The AGFI values of the constructs were mostly above 0.90 as
well, and the values of the NFI, an index used for comparative purpose,
are also satisfactory. The results, therefore, suggest that the
measurement scales for each factor possesses construct validity (Hair et
al. 1995).
4.2.4. Correlation Analysis
Correlation analysis is performed to determine the direction and
strength of correlations between scales that have proved uni-dimensional
by confirmatory factor analysis. The absence of 1.0 in the confidence
interval for the correlation coefficient of two constructs is an
indication of a positive correlation (Hair et al. 1995). The
coefficients of correlation obtained from the final confirmatory factor
analysis are given in Table 6.
4.3. Research Model and Hypothesis Testing
The estimation of the research model proposed in this study
resulted in the chi-square value of 7.098, p-value of 0.129 and a GFI of
0.989, suggesting a good model fit. The results of the test where the
fit indices were: AGFI = 891, RMR = 0.040, NFI = 937 are as shown in
Fig. 5. The path analysis performed on the structural model revealed
that the average path score, excluding the paths between social benefits
and satisfaction, between perceived risks (service risk and cost risk)
and satisfaction, and between cost risk and consumer acceptance
characteristics, had a positive value. The path analysis, further,
revealed that the three benefits--service-specific benefits, economic
benefits and social benefits--had an influence on consumer acceptance
characteristics.
[FIGURE 5 OMITTED]
The results of the hypothesis testing are summarized in Table 7. As
service-specific benefits, social benefits and economic benefits proved
to have a positive influence on the consumer acceptance of mobile
services and the satisfaction with mobile services, all three hypotheses
related to perceived benefits [H 1-1, H 1-2 & H 1-3] were accepted.
As for [H 2-1], only a portion of the hypothesized content possessed
explanatory power; service risk had a positive influence on the
acceptance of mobile services, but had a negative influence on
satisfaction. [H 2-2] was accepted in its entirety, as cost risk had a
negative influence on both acceptance behavior and satisfaction.
Finally, [H3] was also accepted, as the consumer acceptance of mobile
services proved to have a positive influence on the level of
satisfaction.
An additional analysis found that these variables influencing
consumers' acceptance behavior with mobile services also indirectly
influenced each other. Perceived benefits such as service-specific
benefits, economic benefits and social benefits, in particular,
influenced each other in a positive fashion. Meanwhile, perceived risks
such as service risk and cost risk influenced each other in a negative
manner, suggesting that cost risk has a sizeable influence on service
risk. This result can be understood as an indication that
consumers' avoidance of cost risk potentially reinforces their risk
aversion at the level of service.
5. Conclusion
This study was an empirical investigation of consumers' usage
behavior with regard to mobile telecommunications services. The test of
the research model constructed to explain factors influencing
consumers' acceptance indicated that expected benefits and risks of
a new mobile service were the most important determinants of acceptance
behavior. This would suggest that consumers' wariness of using
mobile services stem to a large degree from the tendency of risk
avoidance. The importance of expected benefits may also explain why
consumers tend to use only some of the many seemingly exciting new
mobile services made available thanks to the technological progress; a
selectiveness hardly affected by the level of knowledge of information
technology and information skills. The implication of this behavioral
tendency for service providers is that they need to adopt a marketing
strategy which is centered on reducing perceived risk factors associated
with using mobile services. Also, given the important role of perceived
benefits in consumers' purchase decision, service providers must
look to develop services that are more accessible and diversify their
content offerings to better meet the growing information demand among
consumers.
One limitation of this study is that we did not consider
differences that may exist between individual mobile carriers, in terms
of service characteristics, as its focus was to understand factors
influencing mobile services' acceptance behavior. Notwithstanding,
as our estimation of consumers' usage behavior with mobile services
was based on data from the age and social groups that are the most
active users of wireless internet, we expect our results to be useful
for service providers in developing target group-specific marketing
strategies. Future research can overcome the limitations of the present
research and provide a more comprehensive understanding of
consumers' acceptance mechanism by considering also factors
influencing the adoption of mobile services among users of GSM-type
mobile telephony, and not just among users of CDMA telephony, whose use
is limited to Korea, Japan and parts of the US.
doi: 10.3846/13928619.2011.557859
Received 02 November 2009; accepted 11 January 2011
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Yeong-Wha SAWNG is a senior researcher of the Service Strategy
Research team at the Electronics and Telecommunications Research
Institute (ETRI), Korea. He received an MIM in Management from Whitworth
University, USA and a PhD in Technology Management from Hanyang
University Seoul, Korea in 1995 and 2006, respectively. He joined ETRI
in 2000, and has been working in the areas of digital convergence, IT
policy, mobile application, technology management and business strategy.
His research interests also include high-tech. marketing, technology
management strategy, e/m-Biz model and consumer behaviour. He has been
published in several international and Korean journals.
Seung-Ho KIM is the vice president of Korea Institute of Industrial
Evaluation, which does consulting and designing for firm organisation.
He received an MS in Business Administration at Seoul National
University, Korea in 1998. His research interests are in the fields of
technology innovation, work group design, strategic management of
technology and R&D project management.
Jungmann LEE obtained his PhD in Economics from the City University
of New York. His research has focused on the areas of technology policy,
R&D management, and the economics of technology innovation at the
Electronics and Telecommunications Research Institute. He has also
served as an advisor for various projects (mid-long term IT technology
policy, the technology roadmap of information and telecommunications and
IT HRD Policy) of the Ministry of Information and Communication, Korea.
He is an assistant professor at the Department of Digital Business at
Hoseo University.
Young Sam OH received his BA from the HanKook University of Foreign
Studies and MBA from Inha University in 1992 and 1995, respectively. For
over 9 years, he has taught high-tech. marketing as an adjunct professor
at Woosong University. He is currently a Ph. D candidate of Inha
University. His research is focused on IT and Service Management.
Yeong-Wha Sawng (1), Seung-Ho Kim (2), Jungmann Lee (3), Young Sam
Oh (4)
(1) Electronics and Telecommunications Research Institute,
Yuseong-gu, Daejon, Korea (2) Korea Institute of Industrial Evaluation,
Daegu, Korea (3) Hoseo University, Anseodong 268 Cheonan city, Chungnam
Province, Korea (4) Inha University, Incheon, Korea E-mails: (1)
ywsawng@gmail.com; (2) kshuri@gmail.com; (3) mann@hoseo.edu
(corresponding author); (4) yso@inha.edu
Table 1. Comparison with Previous Research Models
Previous Research Model Research Objective Weaknesses
Adoption theory Explain the No quantification or
diffusion of prioritization at
innovations the consumer level
of product
attributes
Diffusion Theory Explain the No rational
diffusion of explanation provided
innovations at the aggregate
level as to the
success/failure of
diffusion
Model for the explanation Explain consumers Overall ill-adapted
consumers' acceptance reasons for using/ to m-services
or not using a
technology product
Theory of perceived risk Explain acceptance Only pertinent to
through perceived characterization
risks risk of
Table 2. Factors Influencing Acceptance
Perceived product Factors of influence
attributes
Perceived Usefulness [] Value Added (e.
g. Fun Factor,
Information)
[] Emotions (e.g.
Feeling of
Independence)
[] Information
Quality (e.g.
Timeliness)
Perceived Ease of Use [] Initial Operation
(e.g. Registration,
First Configuration)
[] Usability of
Service (e.g.
Intuitive Handing,
Idle Time)
[] Usability of
Terminal Equipment
(e.g. Display,
Keypad)
Perceived costs [] Money costs (e.g.
Purchase costs,
Basic Rates, Usage
Costs)
[] Transparency
(e.g. Tariff Models,
Cost per Minute/
Request/Bit)
[] Health Concerns
(e.g. Dangerous
Radiation)
Perceived Network Effects [] Network Coverage
(e.g. Dissemination,
Roaming)
[] Terminal
Equipment (e.g.
Design, Size,
Colour)
[] Image (e.g.
Service as Status
Symbol, Group
Affiliation)
Table 3. Results of Reliability Test
Constructs and Variables Number Cronbach
of alpha
items
Perceived ease of use EOU 4 0.752
Service Perceived costs CST 4 0.733
characterstics Perceived usefulness USE 3 0.736
Perceived network effects NET 3 0.730
Perceived Service-specific benefits SVC 4 0.727
benefits Social benefits SCL 3 0.737
Economic benefits ECO 2 0.742
Perceived risks Cost risk SRK 2 0.743
Service risk CRK 2 0.717
Service SAT 3 0.741
satisfaction
Table 4. Results of Factor Analysis on Mobile Service Characteristic
Variables
Variables Factor 1
EOU1 Ease of 0.833
information
access
Perceived EOU2 Information 0.878
ease of use EOU3 quality
Quality 0.798
of product
information
EOU4 Pleasure of use 0.788
CST1 Saving on
Service Perceived CST2 Saving on usage
characterac- costs cost; Perceived
teristics usefulness;
Perceived costs
CST3 Transparency of
price
components
CST4 Confidence in
the seller
Perceived USE1 Ease of initial
usefulness setup and use
USE2 Usefulness of
services
USE3 Ease of
handling
NET1 Usable anytime
Perceived NET2 anywhere;
network Diversity of
effects handset in
design and size
NET3 Availability of
other linked/
partnered
services
Eigen value 2.858
% of variance 20.413
Cumulative % of variance 20.413
Variables Factor 2
Perceived EOU1
ease of use EOU2
EOU3
EOU4
Service Perceived CST1 0.766
characterac- costs
teristics CST2 0.782
CST3 0.591
CST4 0.766
Perceived USE1
usefulness USE2
USE3
Perceived NET1
network
effects NET2
NET3
Eigen value 0.640
% of variance 18.855
Cumulative % of variance 39.268
Variables Factor 3 Factor4
EOU1
Perceived EOU2
ease of use EOU3
EOU4
CST1
Service Perceived CST2
characterac- costs
teristics
CST3
CST4
Perceived USE1 0.672
usefulness
USE2 0.721
USE3 0.780
NET1 0.773
Perceived NET2 0.713
network
effects
NET3 0.729
Eigen value 2.075 1.861
% of variance 14.878 13.294
Cumulative % of variance 54.086 67.380
Table 5. Results of Confirmatory Factor Analysis
Num-
Constructs ber of DF
items
Perceived ease of use 4 2
Service char- Perceived costs 4 2
acteristics Perceived usefulness 3 5
Perceived network 3 0
effects
Perceived benefits Service-specific 4 2
benefits
Social benefits 3 2
Economic benefits 3 0
Perceived Service risk 3 6
risks: Cost risk, Service 3 2
Service risk satisfaction
Service 3 2
satisfaction
Constructs [chi square] p
Perceived ease of use 15.3 0.00
Service char- Perceived costs 1.15 0.56
acteristics Perceived usefulness 8.1 0.15
Perceived network 0.0 0.0
effects
Perceived benefits Service-specific 12.3 0.00
benefits
Social benefits 18.2 0.00
Economic benefits 0.0 1.00
Perceived Service risk 2.8 0.32
risks: Cost risk, Service 1.6 0.43
Service risk satisfaction
Service 1.6 0.46
satisfaction
Constructs RNSR GFI AGFI
Perceived ease of use 0.033 0.96 0.82
Service char- Perceived costs 0.013 0.99 0.98
acteristics Perceived usefulness 0.033 0.98 0.95
Perceived network 0.0 1.0 1.0
effects
Perceived benefits Service-specific 0.037 0.97 0.86
benefits
Social benefits 0.042 0.95 0.78
Economic benefits 0.0 1.0 1.0
Perceived Service risk 0.013 0.99 0.93
risks: Cost risk, Service 0.033 0.99 0.98
Service risk satisfaction
Service 0.023 0.99 0.98
satisfaction
Constructs NFI
Perceived ease of use 0.96
Service char- Perceived costs 0.99
acteristics Perceived usefulness 0.96
Perceived network 1.0
effects
Perceived benefits Service-specific 0.95
benefits
Social benefits 0.90
Economic benefits 1.0
Perceived Service risk 0.99
risks: Cost risk, Service 0.99
Service risk satisfaction
Service 0.99
satisfaction
Table 6. Correlation Matrix Analysis
Pearson's EOU CST USE UFT SVC SCI
coefficient
of correlation
1 .277 * .291 * .306 * .449 * .381 *
EOU
(.000) (.000) (.000) (.000) (.000)
.277 * 1 .533 * .289 * .306 * .341 *
CST
(.000) (.000) (.000) (.000) (.000)
USE .291 * .533 * 1 .330 * .325 * .320 *
(.000) (.000) (.000) (.000) (.000)
NET .308 * .239 * .330 * 1 .270 * .413 *
(.000) (.000) (.000) (.000) (.000)
SVC .449 * .306 * .325 * .270 * 1 .445 *
(.000) (.000) (.000) (.000) (.000)
SCL .381 * .341 * .320 * .413 * .445 * 1
(.000) (.000) (.000) (.000) (.000)
ECO .172 * .354 * .302 * .118 .227 * .162 *
(.016) (.000) (.000) (.099) (.001) (.023)
SAK .214 * .119 .137 * .213 * .423 * .255 *
(.003) (.095) (.006) (.001) (.000) (.000)
CRK .119 * .405 * .388 * .355 * .364 * .331 *
-0.005 (.000) (.000) (.000) (.000) (.000)
SAT .158 * .431 * .304 * .129 * .291 * .257 *
(.025) (.000) (.000) (.077) (.000) (.000)
Pearson's ECO SBK CRK SAT
coefficient
of correlation
.172 * .214 * .119 * .158 *
EOU
(.016) (.003) (.005) (.026)
.354 * .119 * .406 * .431 **
CST
(.000) (.095) (.000) (.000)
USE .302 * .137 * .333 * .304 **
(.000) (.008) (.000) (.000)
NET .118 .243 * .355 * .129
(.099) (.001) (.000) (.071)
SVC .227 * .423 * .364 * .291 **
(.001) (.000) (.000) (.000)
SCL .162 * .255 * .331 * .267 **
(.023) (.000) (.000) (.000)
ECO 1 .138 .453 * .430 **
(.054) (.000) (.000)
SAK .138 1 .225 * .106
(.054) (.001) (.132)
CRK .453 * .225 * 1 .295 **
(.000) (.001) (.000)
SAT .430 * .106 .295 * 1
(.000) (.132) (.000)
* the coefficient of correlation is significant at the level of 0.05
(bidirectional),
** the coefficient of correlation is significant at the level of
0.01 (bidirectional).
+ Parenthesis is significance probability, the size of sample = 197.
Table 7. Results of Hypothesis Testing and Analysis of
Acceptance Influencing Factors
Hypotheses Test Results; Path Coefficients
Accept reject
H Service- Service- 0.18 Accepted
1-1 specific specific
benefits have a benefits a
positive Acceptance
influence on behavior
the acceptance
of an m- Service- 0.15
service and the specific
degree of benefits a
satisfaction Satisfaction
felt by
consumers
H 1-2 Social benefits Social benefits 0.18 Accepted
have a positive a Satisfaction
influence 0.03
Social benefite
a Acceptance
be-on the
acceptance of
an m-service
and the havior
degree of
satisfaction
felt by
consumers
H Economic Economic 0.11 Accepted
1-3 benefits have a benefits a
positive Acceptance
influence on behavior
the acceptance
of an m- Economic 0.37
service and the benefits a
degree of Satisfaction
satisfaction
felt by
consumers
H 2-1 The service Service risk a 0.05
risk associated Acceptance
with an in- behavior
service has a Service risk a -0.07 Rejected
negative Satisfaction
influence on
its acceptance
and the degree
of satisfaction
felt by
consumers
H 2-2 The cost risk Cost risk a -0.08
associated with Acceptance
an m-service behavior -0.01 Accepted
has a negative
influence on
its acceptance
and the degree
of satisfaction
felt by Cost
risk a
Satisfaction
consumers
H3 The Acceptance 0.46 Accepted
characteristics behavior a
of consumer Acceptance
acceptance of behavior
m-services have
a positive
influence on
satisfaction
