INTRINSIC AND EXTRINSIC MOTIVATION IMPACT ON
MOBILE CLOUD COMPUTING CONTINUANCE INTENTION
Mirsobit Mirusmonov, School of Business, Yeungnam University, Gyeongbukdo, Republic
of Korea, [email protected]
Changsu Kim, School of Business, Yeungnam University, Gyeongbukdo, Republic of Korea,
[email protected]
Abstract
The well-grounded advancement of information systems can offer most users more comprehensive
and satisfying functions that make an individual’s daily involvements simpler and easier. The mobile
cloud computing acceptance and continued usage nature investigation is one of the most timely
research domains for furthering our understanding in IS. The focus is on the integration of the
intrinsic and extrinsic motivation dimensions that mobile subscribers experience to proposed
expectation-confirmation model. Practically, the study investigates the moderating effect of mobile
subscriber’s exposure on the relationship within our core IS continuance model with integrated
intrinsic and extrinsic motivation. A total of 550 acceptable responses, coming from two staged field
survey, targeted on respondents among Korean company employees. The findings provide a basis for
an enhanced theory on users’ continued use of an innovation and its applications to mobile cloud
computing. This study is expected to offer incremental insights from Korean mobile users’ experience
of conventional web-based services worldwide before they grow into the new mobile cloud business.
Keywords: Mobile Cloud, Mobile Cloud Applications, Intrinsic Motivation, Extrinsic Motivation,
Expectation-Confirmation Theory, Satisfaction
1.
INTRODUCTION
The topic of understanding the adoption of information technology including mobile cloud computing
(MCC) has long been considered an important research area for information system (IS) (Davis, 1989).
However, adoption is not considered equivalent to continuous use as the latter confirms the
satisfaction from using it (Ahmad et al., 2010; Hsu et al., 2004). This means that the benefit of the
system could only be gained if it is frequently and productively used. Therefore, understanding the
factors influencing the user‘s intention to continue using MCC is a critical issue for researchers and
practitioners.
As a complex technology, MCC requires extensive research, which involves user motivation for this
study (Boerger, 2011; Huang et al., 2010; Li et al., 2009). To date, most studies on MCC have dealt
with extensive survey on architecture, technology characteristics, and advantages that could be well
interpreted as motivating forces to spread MCC among active mobile users (Klein et al., 2010; Motlik,
2008; Nordman and Liljander, 2003). However, none of these studies went beyond the adoption and
implementation stage. We could not succeed to come across with a report having unambiguously
proposed MCC model that would concentrate on continuance intention evaluation (Jarvenpaa et al.,
2010; Kovachev et al., 2010).
More recently, researchers have attempted to empirically test the determinant structure of continued
IT usage behavior (e.g., Bhattacherjee, 2001; Bhattacherjee and Premkumar, 2004; Karahanna et al.,
1999). Our study builds on these efforts by extending the ―Expectation-Confirmation Theory‖ in
mobile IT domain — a theoretical model by Bhattacherjee (2001) that was developed specifically to
understand users‘ continued IS usage behavior. Evidently, we apply the ECT framework of IS
continuance (Bhattacherjee, 2001) as the theoretical foundation of our model. We choose ECT based
framework in our study for two reasons. Firstly, ECT based IS continuance framework considers both
pre and post-acceptance behavior to explain IS continuance. Bhattacherjee (2001) argues that many of
the current continuance studies view continuance as an extension of acceptance behaviors (i.e., they
employ the same set of pre-acceptance variables to explain both acceptance and continuance
decisions), and thus unable to explain why some users discontinue IS use after accepting it initially.
Secondly, we may argue that building our framework on the ECT based framework gives us the
opportunity to include external constructs based on intrinsic motivation theory. Here, the behavioral
intention is predicted by motivation dimensions. Besides outcome expectations is argued to fit in the
IS continuance context because it is the only belief that is demonstrated to consistently influence user
intention across temporal stages of IS use (Venkatesh et al., 2011). As suggested by Taylor and Todd
(1995), factors believed to be relevant to the technology usage can be derived from different streams
of literature, such as diffusion of innovation and consumer behavior. Thus, we believe that our
framework built on the ECT framework is theoretically grounded.
2.
LITERATURE REVIEW
2.1.
Theoretical background on MCC
Previous research on MCC dealt primarily with the definition (Huang et al., 2010), nature (Jarvenpaa
et al., 2010; Klein et al., 2010) and entire concept of building MCC (Boerger, 2011; Satyanarayanan,
2010). Different studies hold different views, and there are several existing definitions of MCC
(Boerger, 2011; Huang et al., 2010; Jarvenpaa et al., 2010; Klein et al., 2010; Kovachev et al., 2010;
Li et al., 2009). The definitions of MCC can be classified into two groups. The first refers to data
storage and processing outside the mobile devices (Satyanarayanan, 2010). Mobile devices are simply
terminals in cloud computing, only intended to provide a more convenient way of accessing a range of
services in the cloud (Nysveen et al., 2005). The second type of definition refers to computing where
data storage and processing are also carried out on mobile devices (Boerger, 2011). Several authors
discussed the advantages of using mobile hardware for cloud computing over using traditional
hardware (Motlik, 2008; Nordman and Liljander, 2003). In some studies, MCC was first referred to as
an infrastructure, where data storage and processing could take place outside the mobile device,
enabling a new class of applications , particularly context-aware mobile social networks (Boerger,
2011; Klein et al., 2010; Nordman and Liljander, 2003; Nysveen et al., 2005; Satyanarayanan, 2010).
With the mobile cloud, the users just need to send their requests for a certain service and the cloud
provides it. The mobile host does not need to use much computing time for complex services
(Turkistany et al., 2009). The other group of papers discusses the nature of difference of MCC from
conventional computing in general (Datamonitor, 2009; Turkistany et al., 2009). Several other
researchers have identified the fundamental challenges in MCC (Huang et al., 2010; Marinelli, 2009).
Few papers have analyzed the forming factors of existing mobile cloud application approaches
(Satyanarayanan, 2010). Complementarily, there are studies arguing security and business continuity
factors as inhibitors to the use of MCC (Reeves, 2009). A set of research unfolds an enormous asset of
evolving MCC for educational and business purposes in rural areas (Cornu, 2010; Motlik, 2008).
The current approach diverges from the majority of prior research, which repeatedly defines and
conceptualizes of MCC (Huang et al., 2010; Jarvenpaa et al., 2010; Klein et al., 2010; Kovachev et al.,
2010; Li et al., 2009). The test bed of this study is the motivation to use MCC, which has not been
embraced by existing studies, because it is relatively a recent development pioneered by global webbased companies such as Google, Apple, Ebay, etc. Nonetheless, with increasing service convergence
across industries, it is expected to become an important platform for mobile applications (Au and
Kauffman, 2008; Li et al., 2009).
2.2.
Theoretical background on ECT and motivation.
To understand the continued use of an IS, we need to understand the role of user‘s cognitive beliefs,
social influence and the socio-technical aspects of the IS (Bhattacherjee, 2001; Davis, et al., 1989;
Fishbein and Ajzen, 1975).The underlying logic of the ECT framework is described by Oliver (1999)
and Bhattacherjee (2001) as follows. First, consumers form an expectation of a specific product or
service prior to a transaction. Second, after a period of consumption, they form perceptions about its
performance. Third, they assess its perceived performance vis-à-vis their original expectation and
determine the extent to which their expectation is confirmed. Fourth, they develop a satisfaction level
based on their confirmation level and the expectation on which that confirmation was based. Finally,
they form a repurchase intention based on their level of satisfaction. It is important to note that all
constructs in ECT except expectation are post-purchase variables, and their assessment is based on the
consumer‘s actual experiences with the seller (Churchill and Surprenant, 1982).
Applying the ECT framework to the IS field is appropriate in which IS users‘ continuance decision is
similar to consumers‘ repurchase decision. First, they both follow an initial activity (acceptance or
purchase); second, they both are influenced by the initial use (of IS or product) experience. And third,
such experience can potentially lead to ex-post-reversal of the initial decision. In order to adapt ECT
into MCC context, several theoretical extensions are required. Such extensions provide unique
opportunities for theory refinement. They can potentially explain IS continuance decisions better than
using the original ECT alone.
Consecutively, along with ECT the intrinsic-extrinsic motivation perspectives from Deci and Ryan
(1985) is identified as background framework for MCC continued use study. Incorporating contextual
variables into a multistage model would deepen our understanding of the subsequent influences of
these contextual variables at later periods (Venkatesh et al., 2011). The intrinsic-extrinsic motivation
is derived primarily from the economics and psychology literature (Deci, 1975; Deci and Ryan, 1985;
Roca and Gagné, 2008). Intrinsic and extrinsic motivational factors are crucial for decision makers
(Bandura, 1986; Compeau and Higgins, 1995a). The significance of studying motivation is clear as it
offers a valuable association: motivation produces. Although many times motivation is treated as a
single or first-order construct, it is evident that people are moved to act by very different factors. We
define motivation, similar to Deci (1975), as a state that is influenced. Further, it must be pointed out
that there is a clear distinction between motivation and personality and emotion (Deci and Ryan,
1985). The motivation studies posit that motivation can be formed by external factors (e.g. strong
external coercion) and by internalized factors (e.g. value an activity), these are called extrinsic and
intrinsic factor respectively (Teixeira et al., 2012). True to previous studies we view this interplay as a
behavior continuum where one could be highly extrinsically motivated or highly intrinsically
motivated. Clearly these two motivations factors are linked (Kim et al., 2004; Teixeira et al., 2012).
As previously expressed, our main argument for extending the theory of expectation-confirmation
with core concepts from intrinsic-extrinsic motivation is to move MCC research in the direction of
understanding how mobile user-centric factors influence the mobile subscribers MCC use continuance
decision. The main proposition is that the concepts from intrinsic-extrinsic studies have a considerable
potential in explaining mobile user‘s continuance intention.
3.
RESEARCH MODEL AND HYPOTHESES
The model for this research (Figure 1) is an extension of the original ECT based on intrinsic-extrinsic
motivation complement. The strength of ECT is that it emphasizes innovation pre-adoption
expectations and post-adoption confirmation beliefs (Hung et al., 2011; Venkatesh et al., 2011).
Intrinsic and extrinsic motivation on the other hand emphasizes basic need fulfillment and
development of genuine user-centric motivation, so is an important element in this theory (Roca and
Gagné, 2008). As it appears from this, ECT with motivational impact has both common and distinct
factors, and the latter group of factors does that these two theories can be viewed as complementary to
each other. External constructs, the extended part of the model, are the constructs of interest because
they operationalized the question of the motivation with which people find reasons to continually
exploit MCC. When people get involved in the activity encouragingly, this would be either the
intrinsic or extrinsic motive for them and this would increase the likeliness to continually make use of
mobile cloud services in the future. We further consider not only how outcome expectations and
confirmation impact satisfaction, but also how these mediating variables affect longer-term
continuance intention (Kim et al., 2004). To achieve the research goal, a research model is introduced
to define the relationships among the variables studied, and relevant hypotheses are proposed. The
research model and accompanying hypotheses are presented below.
Intrinsic motivation
Self-efficacy
Familiarity
Outcome
Expectations
Affin ity
Satisfaction
Extrinsic motivation
Reputation
Social
interaction
Technology
interactivity
Confirmat ion
Continuance
intention
Figure 1.
Proposed research model
3.1.
Intrinsic Motivation

Self-efficacy hypothesis
Wood and Bandura (1989) assert that continued use of a system is significantly influenced by one‘s
perceived ability to complete it, better known as self-efficacy (Bandura, 1997). In IS, the importance
of self-efficacy has long been realized (Davis, 1989), and when dealing with computing, has been
termed computer self-efficacy (Compeau and Higgins, 1995). In the realm of MCC, self-efficacy is
the belief in one‘s capability to organize and execute courses of mobile web actions to produce given
attainments. This belief in ability is distinct from one‘s familiarity (Eastin and LaRose, 2000). Prior
studies have examined the impact of self-efficacy on web-based instruction (Joo et al. , 2000),
information search (Kuo et al., 2004), and electronic services (Hsu and Chiu, 2004). The relationship
between self-efficacy and usage of m-cloud seems obvious. Operating mobile cloud services present a
somewhat complex environment, requiring considerable self-efficacy to operate successfully –
learning to navigate and exploit relevant data. In order to sustain continued use of MCC, users must
have adequate levels of self-efficacy. Users‘ continuance intentions will also be influenced by the
ability of the MCC to keep him focused in manipulation activities. Further, self-efficacy researchers
emphasized that self-efficacy beliefs should be assessed in such a way that the beliefs correspond to
the targeted performance and domain of interest. Recent work in the IS literature has assessed the
level of measure of efficacy. Self-efficacy is known to influence outcome expectations and
performance (Compeau and Higgins, 1995). Eastin and LaRose (2000) argued self-efficacy to be
essential predictor of outcome expectancy that consecutively affects performance (confirmation).
H1a. Self-efficacy has a positive impact on outcome expectations.
H1b. Self-efficacy has a positive impact on confirmation.

Familiarity hypothesis
The other way mobile users subjectively reduce uncertainty and simplify their relationships with
mobile services is familiarity. Familiarity is an understanding, often based on previous interactions,
experiences, and learning of what, why, where and when others do what they do (Gefen, 2000).
Familiarity deals with an understanding of the current actions of other people or of objects. Since
favorable expectations are naturally context-dependent, understanding the given context involved
(familiarity) is often an important antecedent (Luhmann, 1988). Conversely, without familiarity with
the context, outcome expectations cannot be adequately anchored to specific favorable behaviors and
thus cannot be as strongly conferred. In the case of ebay.com, for example, mobile users‘ familiarity
with the concept of mobile cloud applications could enable them to entertain perceptions concerning
the expectancy measure they expect from the mobile cloud computing. Conversely, mobile
subscribers who are not aware of cloud solution on move (lack of familiarity) have no reason to hold
such expectation.
Another hypothesized impact of familiarity on MCC satisfaction is that familiarity not only provides a
framework for future expectations, but also lets mobile users create concrete ideas of what to expect
based on previous interactions (Thong et al., 2006). The reason for this is that familiarity gauges the
degree that prior experience has been understood. Since in many cases prior experience is the basis of
expectancy-confirmation (Gagné and Deci, 2005), familiarity can both confirm, when the experience
was favorable, or ruin, when not (Gefen, 2000). In the case of ebay.com, for example, people familiar
with eBays`s mobile cloud service had probably previously experienced cloud application use and in
the process had likely noticed that the mobile service behaved in accordance with their favorable
expectations. Thus, we posit:
H2a. Familiarity has a positive impact on outcome expectations.
H2b. Familiarity has a positive impact on confirmation.

Affinity hypothesis
Researchers depict affinity as an individual‘s learned predisposition to respond in a consistently
favorable or unfavorable manner to a given object, and that it greatly affects the performance of a
user‘s positive feelings behavior (Azjen and Fishbein, 1980). To follow-up, Lancaster (1966) noted
that affinity is the driver of consumer perceptions. Triandis (1979) described affinity as an
individual‘s positive or negative behavior towards innovation adaptation. Triandis further stated that
affinity portrayed the perceptions of outcome of e-commerce, adaptation features, risk and privacy,
and personal preferences (Triandis, 1979). People who have intrinsically based personal preferences
for a service or a device are more willing to involve with this service and the device and there will be
more interactions occurred between a user and the service or devices (Wakefield and Whitten 2006).
In this sense, affinity in MCC is an important construct that is useful for studying the adoption of
mobile cloud services because it affects the users‘ motivation about the outcome of MCC (Alia et al.,
2007; Stafford et al., 2010). When consumers enter a new market, they generally show little evidence
of a product or service preference (Bouwman et al., 2007). The consumers‘ choices are also largely
affected by their most recent subjective evaluation (Fishbein and Ajzen, 1975). Therefore, we
hypothesize that the affinity is positively related to the outcome expectations and confirmation:
H3a. Affinity has a positive impact on outcome expectations.
H3b. Affinity has a positive impact on confirmation.
3.2.
Extrinsic Motivation

Reputation hypothesis
Reputation is defined as the current assessment of an entity‘s desirability as established by some
external person or group of persons (Livingston, 2005). An entity, such as a firm, may attempt to
influence its reputation through mechanisms such as signaling (Kirmani and Rao, 2000). However,
reputation is ultimately established by parties external to the firm (Fombrun, 1996; Josang, 2006). The
ability of the firm to manipulate its own reputation is limited by the willingness of parties external to
the firm to include these influence attempts in their overall assessment (Josang, 2006; Livingston,
2005). Increased assurance that mobile cloud service vendor will not act opportunistically is critical
when engaging in mobile commerce since the separation between payment and delivery necessarily
puts the mobile users at increased risk. A strong positive reputation on behalf of the mobile cloud
vendor reduces the probability that the vendor will act opportunistically. For instance, reputation of
eBay might entail providing credit card information to mobile cloud service based on the guarantyless favorable expectations that the information will be in appropriate use. Consequently, mobile
subscribers can perceive mobile cloud transactions as useful. Moreover, a strong positive reputation
provides a strong assessment of cloud service perceived outcome. This kind of confirmation arises
from the increase in assurance that the seller will complete the transaction as contracted. This gives
the reputable mobile cloud service vendor a strategic advantage over others‘ who are having less
positive reputations (Josang, 2006). These observations lead to the following hypotheses:
H4a. Reputation has a positive impact on outcome expectations.
H4b. Reputation has a positive impact on confirmation.

Social interaction hypothesis
In advance, social interactions refer to particular forms of externalities, where the actions of a group
of people affect an individual‘s preferences (Scheinkman, 2008; Teng et al., 2011). In the wave of
human centered pervasive computing, m-cloud computing is becoming a major driving force to foster
social interactions among people with its powerful communication and sensing capability (Cook et al.,
2009; Lasica, 2009; Liu et al., 2011). With the proliferation of mobile devices in m-cloud computing
technology, it is intuitive and natural for users to socially interact with their collaborators or
competitors in multi-party conferencing, productivity or gaming applications (Häkkilä et al., 2011;
Kim and Lee, 2011). People today are more interested in being connected to each other, whether it
might be through an online forum, a community or mobile access (Lasica, 2009). Therefore, along
with the greater social interaction achieved via the use of m-cloud services, users will digest it as a
utility to engage in m-cloud computing (Häkkilä et al., 2011) On the other hand, several studies have
reported social interaction as the reason for the use of mobile devices (Häkkilä et al., 2011; Liu et al.,
2011; Nysveen et al., 2005; Scheinkman, 2008). As a consequence of m-cloud expansion, the positive
attitude to this innovation has been increased by the impact of the m-cloud social interaction value,
which is generally associated with changes in technology, socioeconomic, and cultural/ethnic factors
within society (Bianco, 2009; Teng et al., 2011). In particular, m-cloud computing-based social
networks have the capacity to fundamentally change healthcare in ways that are just beginning to be
discovered, similar to monitoring patients at home efficiently (Scheinkman, 2008). Moreover, mcloud computing as part of a smart environment can also be used for valuable functions, such as athome health monitoring and social assistance generating positive attitude (Scheinkman, 2008).
Consequently, m-cloud computing-based social interaction opportunity affirms the positive attitude
towards m-cloud computing technology. These observations lead to the following hypotheses:
H5a. Social interaction will have a positive impact on the outcome expectations.
H5b. Social interaction will have a positive impact on confirmation.

Technology interactivity hypothesis
The present case focuses on MCC with mobile devices. Many studies have analyzed numerous
characteristics of mobile computing and cloud computing (Sullivan and Drennan, 2005). If
crosscutting concerns are set aside, the main characteristic is technology interactivity (Zeal et al.,
2010). MCC characteristics have the potential to directly affect both the outcome expectations of the
mobile cloud and the performance (Zmijewska et al., 2004). Mobile interactivity can be seen as a
result of the interaction of mobile technical elements (Zeal et al., 2010). Technology interactivity is
believed to have an indirect effect on the actual use of MCC because of the reflective performance
and the expectations from mobile cloud applications. For example, for users, mobile interactivity on
all devices and networks generates undeniable expected outcome for it. Mobile users with can easily
access ubiquitous environments for further manipulation and benefit from options such as time and
money savings (Zmijewska et al., 2004). By this, technology interactivity feature avails MCC positive
outcome for a user consideration. The removed difficulties and barriers related to interactivity issue
may serve to drive extended attraction to MCC as a whole. This phenomenon in turn, nourishes an
improving performance of mobile cloud service. Therefore, it is hypothesized that the technology
interactivity is positively related to the outcome expectations and performance:
H6a. Technology interactivity will have a positive impact on the outcome expectations.
H6b. Technology interactivity will have a positive impact on confirmation.
3.3.
Endogenous Variables

Outcome expectations hypothesis
Previous studies indicate that individuals are more likely to engage in behaviors that they expect will
be beneficial (Bandura, 1986; Kim et al., 2004). These studies directly concerned with measuring
outcome expectancy in the IT literature are limited in number. Outcome expectations are
demonstrated in the e-commerce context clearly through the increased utilization of this technology
by consumers who expect a higher quality, lower prices, extended availability, and a wider variety of
products while shopping online (Chen et al., 2009; Thong et al., 2006). The extra value users expect
out of simple tasks they are capable of performing will create a major motivating factor for them to
use the MCC. Outcome expectations essentially summarize the benefits expected by users from using
a system (Kim et al., 2004). Outcome expectancy is described as the degree to which a user
anticipates that a particular system can improve his or her work performance (e.g. increase
productivity, improve efficiency) (Davis, 1989). Outcome expectancy is thus a belief about the
consequences of behavior. Confirmation could be adjusted by Outcome expectations, especially when
a user‘s initial expectations are unstable because he or she is uncertain what to expect from the usage
of the IS (Hong et al. 2006). Related researches also proved that users will have afterward expectation,
and the expectation will influence on users‘ satisfaction towards the information system
(Bhattacherjee, 2001). In related literature on post-acceptance model of IS continuance, it has been
proved that Outcome expectations strongly relates to satisfaction degree (Bhattacherjee,
2001).Consistent with ECT (Bhattacherjee 2001), we assert that outcome expectations to be a salient
factor that influences a user‘s satisfaction as well. So, expectations are considered a primary motivator
of IS acceptance, and ECT suggests that it is also plausible that Outcome expectations can influence
subsequent continuance decisions. This leads to the following hypotheses:
H7a. Outcome expectations is positively related to confirmation.
H7b. Outcome expectations is positively related to users‘ satisfaction.
H7c. Outcome expectations is positively related to users‘ MCC continuance intention.

Confirmation hypothesis
Confirmation refers to the user‘s assessment of the perceived performance of an IS relative to his or
her initial expectations (Kim et al., 2004). Confirmation is positively related to satisfaction with IS
use because it implies realization of the expected benefits of IS usage, while disconfirmation indicates
failure to meet expectations. The confirmation-satisfaction association has been empirically examined
by testing ECT (Bhattacherjee, 2001; Hsu et al., 2004; Lin et al., 2005) in different IS settings. In the
MCC context, a user‘s confirmation implies that she or he has achieved the expected benefits through
the usage of the MCC. Additionally, it is mentioned that expectation before usage and the cognition
performance of the system after usage will together influence on users‘ satisfaction (Thong et al.,
2006). It proved that users‘ ―confirmation‖ will affect ―satisfaction‖ after usage (Bhattacherjee, 2001;
Lin et al., 2005). Therefore, we propose the following hypotheses on confirmation:
H8. Users‘ extent of confirmation is positively related to their satisfaction.

Satisfaction hypothesis
Satisfaction refers to an individual‘s positive or negative feeling about the MCC. Based on ECT,
users‘ IS continuance intention is determined primarily by their satisfaction with prior use.
Bhattacherjee (2001) posits that the construct satisfaction is a transient, experience-specific affect.
Bhattacherjee (2001) argues that the continued usage behavior is different from and possibly more
important than its initial adoption. Many adopters can initially be driven by mandatory pressure but
may discontinue its use in a later stage (Hossain and Quaddus, 2011). Therefore, satisfaction can
significantly influence post-adoption use intention (Bhattacherjee, 2001; Limayem and Cheung, 2008).
Continuance intention to use MCC is an important implication of positive experience and satisfaction
from using it. It is also the result of users confirming the expected results or benefits from using it
(Bhattacherjee, 2001). Hence, the ultimate success of a system is said to depend largely on its
continued use followed by users‘ satisfaction (Bhattacherjee, 2001; Hussain and Quaddus, 2011).
Therefore, we can expect that the intention to continue using MCC can only be strong when
satisfaction from using it has been positive and thus the final hypothesis is stated as:
H9. Satisfaction is positively related to Continuance Intention.
4.
METHOD
To gain sample generalizability across mobile subscribers in network covered environment, two
phases of data collection for the study were adopted for the same target group. The reason is to
validate the proposed research model which has expectation and confirmation/satisfaction elements,
the data for pre-adoption measures (i.e., expectation) and post-adoption measures (i.e.,
confirmation/satisfaction) should have been collected in different time frame from the same
respondents. Compliantly, at initial stage, the questionnaires concerning mobile cloud services were
emailed to various Korean company workers. These potential respondents were randomly contacted
through company directories at www.korcham.net. Both phases survey ran for a 12 week period; from
January 2nd to 31st of March, 2012, which yielded more than 2200 distributed questionnaires. Of
these, a total of 860 responses were received, of which only little over 600 belonged to m-cloud users.
Approximately 50 questionnaires excluded for data analysis after screening questionnaire
incompleteness and inconsistencies. As a result of 550 (the effective response rate - 6.71 percent)
effective subjects indicated that 255 (46.4 percent) were females, and 53.6 percent were males; 132
(24.0 percent) of subjects were students, 60 (10.9 percent) were entrepreneurs while 350 (63.6
percent) of subjects were employed workers. Among the employed individuals, more than 70.5%
were well-off with an annual income of more than $US 30,000. Against the annual earnings and
education ground, the majority of respondents represent the middle class of Korean society (in
compliance with Korea Statistical Information Service, 2010 (KSIS)). The vast majority of subjects
confessed to be highly open to mobile cloud applications (69.6%) with the minority being less aware
(30.4%). Based on the high mobile cloud exposure backed up by statistics of handy digital gadgets
owned by these respondents, it can be concluded that almost every Korean (95.2%) has had cloud
service experience with either smartphones or tablet PCs. Majority (52.4 percent) of the subjects had
prior experience with mobile cloud computing for over a year. Indeed, possession assisted the
respondents to become aggressive and frequent users of m-cloud computing (52.2%).
The design of this survey complies with our theoretical models. The variables identified in the
research model were measured using multi-item indicators which aimed to capture the underlying
theoretical domain of the construct. All items were measured using a five-point Likert-type scale
ranging from strongly disagree to strongly agree. The choice of theoretical constructs to be examined
was determined through a review of the theoretical adoption literature as well as a summary of the
measurement items is provided in appendix.
5.
DATA ANALYSIS AND RESULTS
CFA was most appropriately applied to measures that had been fully developed and their factor
structures validated. The measurement scales were refined through the development of a strategy of
confirmatory models (Byrne, 1998; Joreskog and Sobom, 1996).
Latent factor (First Analysis)
Threshold
selfefficac
y
fa miliarit
y
4
4
affinity
reputatio
n
social
interactio
n
technology
interactivit
y
outcome
expectanc
y
confir matio
n
satisfactio
n
continuanc
e intention
4
4
4
4
4
4
4
4
7.324
27.963
15.323
22.488
10.329
27.725
19.74
/ 0.000
/ 0.000
/ 0.000
/ 0.000
/ 0.000
/ 0.000
/ 0.000
p≥0.0
5
16.15
2
/
0.000
3≥
8.076
12.651
7.837
3.662
13.982
7.661
11.244
5.165
13.862
9.87
RMR
0.05≥
0.026
0.017
0.03
0.022
0.044
0.033
0.044
0.021
0.048
0.47
GFI
0.9≤
0.986
0.995
0.985
0.993
0.974
0.987
0.979
0.991
0.975
0.969
AGFI
0.8≤
0.932
0.977
0.926
0.966
0.871
0.934
0.895
0.953
0.873
0.956
NFI
0.9≤
0.968
0.992
0.965
0.988
0.957
0.976
0.924
0.985
0.955
0.966
IFI
0.9≤
0.972
0.995
0.969
0.991
0.96
0.979
0.93
0.988
0.958
0.971
TLI
0.9≤
0.916
0.985
0.906
0.973
0.878
0.938
0.787
0.963
0.875
0.952
CFI
0.9≤
0.972
0.995
0.969
0.991
0.959
0.979
0.929
0.988
0.958
0.971
RMSE
A
0.05~
0.114
0.053
0.112
0.067
0.154
0.11
0.137
0.087
0.153
0.134
CMIN
/p
CMIN
25.302
/ 0.000
15.67
5
/
0.000
/DF
0.1≥
Latent factor (Final Analysis)
Threshold
selfefficac
y
fa miliarit
y
affinity
reputatio
n
social
interactio
n
technology
interactivit
y
outcome
expectanc
y
confir matio
n
satisfactio
n
continuanc
e intention
3
3
3
3
3
3
3
3
3
3
25.302
31.48
7.324
31.774
15.323
30.251
10.329
27.725
19.74
/ 0.000
/
0.000
/ 0.000
/ 0.000
/ 0.000
/ 0.000
/ 0.000
/ 0.000
/ 0.000
p≥0.0
5
18.71
8
/
0.000
3≥
9.359
12.651
15.74
1
3.662
15.887
7.661
15.126
5.165
13.862
9.87
RMR
0.05≥
0.023
0.041
0.031
0.047
0.04
0.046
0.028
0.049
0.042
0.047
GFI
0.9≤
0.99
0.851
0.984
0.856
0.984
0.857
0.984
0.858
0.864
0.868
AGFI
0.8≤
0.979
0.823
0.965
0.829
0.965
0.829
0.966
0.829
0.835
0.839
NFI
0.9≤
0.979
0.805
0.959
0.808
0.972
0.81
0.965
0.811
0.817
0.825
IFI
0.9≤
0.994
0.87
0.976
0.874
0.983
0.874
0.98
0.873
0.878
0.883
TLI
0.9≤
0.989
0.851
0.96
0.856
0.973
0.856
0.967
0.853
0.858
0.864
CFI
0.9≤
0.993
0.868
0.975
0.872
0.983
0.873
0.98
0.871
0.876
0.882
RMSE
A
0.05~
0.028
0.054
0.051
0.053
0.051
0.054
0.049
0.052
0.055
0.055
CMIN
/p
CMIN
/DF
0.1≥
Table 1.
Result of Confirmatory Factor Analysis
The initial measurement model was analyzed to generate high fitness from final items. The C.R. value
from the measurement items was over +1.96. Therefore, no item needed to be removed. On the other
hand, ten measurement items, such as SELF4 (self-efficacy item), FAML4 (familiarity item), AFFN4
(affinity item), REPT4 (reputation item), SOCINT4 (social interaction item), TECINT4 (technology
interactivity item), OUTEX3 (outcome expectancy item), CONF4 (confirmation item), SATS4
(satisfaction item) and CONTI4 (continuance intention item) were removed due to the insufficient
explanation power (Table 1). All items presented high factor loadings on their underlying
corresponding construct (> 0.40) and had low factor loadings on other constructs (cross-loadings),
except above mentioned. The squared multiple correlation (SMC) index was performed to determine
how well the measurement variables explain the latent variables.
Statistical software AMOS license version 19.0 was used for the CFA, and some widely used
indicators were also used to evaluate the measurement model fitting to the data, such as root mean
square error of approximation (RMSEA), standardized root mean square (RMR), normed fit index
(NFI), goodness of fit index (GFI), comparative fit index (CFI), and adjusted goodness of fit index
(AGFI) (Bagozzi and Yi, 1988; Byrne, 1998; Joreskog and Sobom, 1996). As a result, all the
measured items showed an acceptable fit (χ2 = 963.093, df = 360, CMIN/DF=2.675, RMSEA = 0.050,
NFI = 0.957, CFI = 0.901, standardized RMR = 0.047, GFI = 0.901, AGFI = 0.875), indicating that a
ten-factor model provided a good fit to the data.
In addition, the factor loadings (Standardized Regression Weights) of all measured items were greater
than the recommended value of 0.50 and with each having significance at the 0.001 level (Hair et al,
1998). Discriminant validity was tested using the average variance extracted (AVE); all constructs
indicated a greater than suggested value of 0.50 (Fornell and Larcker, 1981). Not only all constructs
for the model showed high construct reliability, but also the item loadings were very high, at 0.70 and
above (see Table 2).
Latent factor
Self-efficacy
SELF1
SELF2
SELF3
S RW(S tandardized
Regression Weights)
0.683
0.789
0.698
S .E.
0.429
0.479
0.544
Construct
reliability
Variance
extracted
Composite
reliability
0.764
0.525
0.786
Familiarity
Affinity
Reputation
S ocial Interaction
Technology
Interactivity
Outcome
expectations
Confirmation
S atisfaction
Continuance
Intention
FAM L1
FAM L2
FAM L3
AFFN1
AFFN2
AFFN3
REPT1
REPT2
REPT3
SOCINT1
SOCINT2
SOCINT3
TECINT1
TECINT2
TECINT3
OUTEX1
OUTEX2
OUTEX4
CONF1
CONF2
CONF3
SATS1
SATS2
SATS3
CONTI1
CONTI2
CONTI3
0.676
0.819
0.733
0.791
0.648
0.688
0.688
0.792
0.688
0.78
0.784
0.626
0.814
0.767
0.629
0.708
0.808
0.59
0.745
0.785
0.728
0.763
0.779
0.65
0.689
0.698
0.748
0.432
0.506
0.777
0.462
0.517
0.623
0.532
0.369
0.586
0.425
0.41
0.672
0.477
0.492
0.679
0.463
0.782
0.599
0.451
0.397
0.497
0.48
0.436
0.683
0.475
0.396
0.554
0.743
0.555
0.793
0.738
0.506
0.766
0.760
0.525
0.747
0.761
0.538
0.791
0.748
0.549
0.775
0.706
0.501
0.764
0.791
0.567
0.790
0.750
0.537
0.770
0.762
0.507
0.762
Note: S.E. = Standard error; Construct Reliability (CR) = (Σλ i )2 / [(Σλ i )2 + Σvar(εi )]; Average Variance Ext racted
(A VE) = (Σλ 2 i ) / n Where λi is the component loading to an indicator and var(εi ) = 1 - λ 2 i ; n is the number of
items.
Table 2.
Construct reliability and Average Variance Extracted
The items also showed discriminant validity. Findings shows all AVEs for each construct displayed
on a diagonal of the correlation matrix, which are greater than the off-diagonal construct correlations
in the corresponding rows and columns; therefore all constructs share more variance with their
indicators than with other constructs (Bagozzi and Yi, 1988; Fornell and Larcker, 1981). The results
shown in Table 2 and Results provide strong empirical support for the reliability and validity of the
scales in the model.
6.
HYPOTHESIS TEST
Following up the CFA test, structural equations modeling (SEM) was suggested to evaluate the causal
relationships between exogenous variables and endogenous variables (Anderson and Gerbing, 1988).
The results of the SEM presented the completely standardized path coefficients and p value. The core
model of this study (Figure 2) was first assessed using SEM, and all models fit indices, indicating an
acceptance level (χ2 = 990.550, df = 373, CMIN/DF=2.656, RMSEA = 0.048, NFI = 0.921, CFI =
0.908, standardized RMR = 0.043, GFI = 0.905). Seventeen hypothesized paths (H1a–H9) linking
exogenous variables (intrinsic and extrinsic) to endogenous variables (Outcome expectations,
confirmation, satisfaction and continuance intention), and those among the endogenous were
simultaneously estimated by Maximum Likelihood in the AMOS.
Results indicated that six exogenous variables for standardized path coefficients on confirmation a nd
perceived usefulness were significant. In addition, the relationships between endogenous variables
were also tested to be statistically significant, with a standardized value of path coefficients. As
expected, all hypotheses for the core model of this study were well supported (Figure 2).
Self-efficacy
0.137*
0.381***
Familiarity
0.225*
Outcome
Expectations
0.492***
0.171**
0.540**
0.246***
Affinity
0.359***
Satisfaction
(R2 = 0.367)
0.327***
Continuance
Intention
(R2 = 0.412)
0.108*
Reputation
0.203***
0.189***
0.143*
0.172*
Social
Interaction
0.115***
0.104*
Confirmation
0.209*
Technology
Interactivity
NOTE: *p<0.05, **p<0.01, ***p<0.001
Figure 2.
7.
General hypothesis testing
DISCUSSION AND IMPLICATIONS
In order to understand IS post-adoption phenomena for MCC, the ECT perspective focuses on the
effect of a referent that is centered on mobile cloud, captured through satisfaction. This study
extended the expectation-confirmation perspective by including the effects of intrinsic and extrinsic
variables. Our findings showed that both dimension sets play important roles in forming two
mediating factors outcome expectations and confirmation. Furthermore, outcome expectations were
found to be the most important antecedent of continuance intention.
The findings of this study have important implications to both theoretical and practical considerations.
The theoretical contribution of this study is that it pushes the frontier of IS adoption research further
out to mobile users' post-adoption behavior territory by testing the potential utility of newer model in
mobile cloud domain. To our knowledge, this is the first study that has tested whether a user-centric
motivation impacts post-purchase satisfaction through a combined model developed from a
longitudinal viewpoint; it is also the first to empirically examine a motivational model of factors that
influence reuse intentions. Besides, this study bridges important exogenous and endogenous factors
from two theories in the mobile commerce context. Although ECT has been fairly conclusive in
studying the use of information technology, intrinsic-extrinsic based constructs could be a critical
variable set when investigating the continuance use of MCC. This research, based on the analysis
above, has confirmed that the integration of intrinsic and extrinsic motivation into the expectation
confirmation theory is able to provide better insights when applying ECT to the continuance use of
MCC. The revised model can be used in the post acceptance behavior rather than the one-time use
behavior in mobile computing. We believe that this type of motivation study provides comprehensive
work and evidence regarding a mobile subscribers` entire decision-making process from motivation,
expectation to continuance intention.
Furthermore, the study states that a research model based on the concept of outcome expectations and
satisfaction as mediators can be used to predict intention to continue using MCC. Mobile subscribers
are found to exhibit strong intention to further use MCC when they have confirmed their expectations
on mobile cloud they are using and having certain perceptions. A notable finding in this study was the
strong influence of outcome expectations on user intentions. Contrary to argumentation, outcome
expectancy was more predictive of continuance intention than satisfaction in our analysis. We assume
this is because users are expected to form intentions toward using MCC based on a initial expectations
of how it will benefit them, not just on their affective feelings toward using the IS.
The results of this study raise interesting implications for practitioners interested in mobile cloud to
gain a competitive advantage. For example, research shows that intrinsic motivation is considered
important for positive perception of MCC. Merely securing technology capacity does not always
guarantee effective expansion of mobile cloud service. By understanding what factors influence
continued usage and how they affect mobile subscribers` usage behavior; the results shed light on how
service providers can promote a higher usage level of MCC. Our extended ECT perspective
acknowledges user evaluation related to MCC use as antecedents of continuance intention. Based on
this finding, we believe that managers should nourish users on how to use mobile cloud effectively in
order to maximize their expectations, confirmation, and satisfaction with MCC use.
8.
LIMITATIONS AND FURTHER RESEARCH
We state that our research findings must be interpreted in the light of the study's limitations. First
potential limitation is that the current study focuses on intention rather than actual usage behavior.
One major difficulty of this kind of academic research is the collection of objective and credible real
usage data. Mobile service carriers are reluctant to disclose their customers‘ usage data for various
reasons, such as customer privacy protection or possible leakage of critical business information to
competitors. Second limitation, originates in the biases inherent in most online survey-based research.
Although we tried to minimize and test for non-response bias, we accept that non-response bias will
not be completely eliminated. Third, the survey attracted a substantial sample of participants, but in
fact covers only population of South Korea, who exceptionally enjoys advanced mobile technology
and complete 4G network coverage. Furthermore, this study has not looked into the possibility of
better alternative models including conceptualizing attitudes and trust as parallel mediators and other
factors as moderators. Therefore, in future research we may carry out alternative models comparison
study for mobile cloud domain in order to increase robustness of the findings. Finally, in order to
generalize our research results, future researchers may be able to survey users who use mobile social
networks in different countries and carry out cross-cultural comparisons.
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